
"""simple docstring"""
import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
   import jax
   import jax.numpy as jnp
   import numpy as np

   from transformers.modeling_flax_pytorch_utils import (
       convert_pytorch_state_dict_to_flax,
       load_flax_weights_in_pytorch_model,
   )
   from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
   import torch




@require_flax
class    _snake_case  :






       def __init__(					self						:			List[Any]						, snake_case__						:			Any						, snake_case__						:			Optional[int]=1_4						, snake_case__						:			str=7						, snake_case__						:			List[str]=True						, snake_case__						:			List[str]=True						, snake_case__						:			str=True						, snake_case__						:			Any=9_9						, snake_case__						:			List[Any]=3_2						, snake_case__						:			Optional[Any]=5						, snake_case__						:			str=4						, snake_case__						:			List[str]=3_7						, snake_case__						:			Optional[int]="gelu"						, snake_case__						:			Any=0.1						, snake_case__						:			int=0.1						, snake_case__						:			Union[str, Any]=5_1_2						, snake_case__						:			int=0.02						, snake_case__						:			Union[str, Any]=None						, ):






            """simple docstring"""



            __a:						Optional[Any]		    =					parent
            __a:						Any		    =					batch_size
            __a:						Optional[int]		    =					seq_length
            __a:						int		    =					is_training
            __a:						Dict		    =					use_input_mask
            __a:						Optional[int]		    =					use_labels
            __a:						Union[str, Any]		    =					vocab_size
            __a:						Any		    =					d_model
            __a:						str		    =					num_hidden_layers
            __a:						Tuple		    =					num_attention_heads
            __a:						int		    =					ffn_dim
            __a:						Union[str, Any]		    =					activation_function
            __a:						Optional[Any]		    =					activation_dropout
            __a:						Union[str, Any]		    =					attention_dropout
            __a:						Dict		    =					max_position_embeddings
            __a:						Any		    =					initializer_range
            __a:						Any		    =					None
            __a:						List[str]		    =					0
            __a:						Any		    =					2
            __a:						Tuple		    =					1






       def 							__lowercase  (					self						:			List[Any]    ):






            """simple docstring"""



            __a:						List[Any]		    =					np.clip(ids_tensor([self.batch_size, self.seq_length]						, self.vocab_size    )						, 3						, self.vocab_size    )

            __a:						Tuple		    =					None
            if self.use_input_mask:
                 __a:						Union[str, Any]		    =					random_attention_mask([self.batch_size, self.seq_length]    )

            __a:						Dict		    =					XGLMConfig(
                vocab_size=self.vocab_size						, d_model=self.hidden_size						, num_layers=self.num_hidden_layers						, attention_heads=self.num_attention_heads						, ffn_dim=self.ffn_dim						, activation_function=self.activation_function						, activation_dropout=self.activation_dropout						, attention_dropout=self.attention_dropout						, max_position_embeddings=self.max_position_embeddings						, initializer_range=self.initializer_range						, use_cache=snake_case__						, bos_token_id=self.bos_token_id						, eos_token_id=self.eos_token_id						, pad_token_id=self.pad_token_id						, )

            return (config, input_ids, input_mask)






       def 							__lowercase  (					self						:			Dict    ):






            """simple docstring"""



            __a:						Tuple		    =					self.prepare_config_and_inputs()
            __a:						Tuple		    =					config_and_inputs
            __a:						List[str]		    =					{"""input_ids""": input_ids, """attention_mask""": attention_mask}
            return config, inputs_dict






       def 							__lowercase  (					self						:			List[Any]    ):






            """simple docstring"""



            __a:						Any		    =					self.prepare_config_and_inputs()

            __a:						Union[str, Any]		    =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
            __a:						Dict		    =					ids_tensor([self.batch_size, self.seq_length]						, vocab_size=2    )

            return (
                config,
                input_ids,
                attention_mask,
                encoder_hidden_states,
                encoder_attention_mask,
            )






       def 							__lowercase  (					self						:			str						, snake_case__						:			Union[str, Any]						, snake_case__						:			List[str]						, snake_case__						:			Tuple						, snake_case__						:			Union[str, Any]    ):






            """simple docstring"""



            __a:						Tuple		    =					2_0
            __a:						Dict		    =					model_class_name(snake_case__    )

            __a:						Dict		    =					model.init_cache(input_ids.shape[0]						, snake_case__    )
            __a:						Dict		    =					jnp.ones((input_ids.shape[0], max_decoder_length)						, dtype="""i4"""    )

            __a:						str		    =					jnp.broadcast_to(
                jnp.arange(input_ids.shape[-1] - 1    )[None, :]						, (input_ids.shape[0], input_ids.shape[-1] - 1)    )
            __a:						Dict		    =					model(
                input_ids[:, :-1]						, attention_mask=snake_case__						, past_key_values=snake_case__						, position_ids=snake_case__						, )

            __a:						List[str]		    =					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						, dtype="""i4"""    )
            __a:						List[Any]		    =					model(
                input_ids[:, -1:]						, attention_mask=snake_case__						, past_key_values=outputs_cache.past_key_values						, position_ids=snake_case__						, )

            __a:						int		    =					model(snake_case__    )

            __a:						Tuple		    =					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])    )    )
            self.parent.assertTrue(diff < 1e-3						, msg=f"""Max diff is {diff}"""    )






       def 							__lowercase  (					self						:			int						, snake_case__						:			Union[str, Any]						, snake_case__						:			Optional[Any]						, snake_case__						:			List[Any]						, snake_case__						:			str    ):






            """simple docstring"""



            __a:						Any		    =					2_0
            __a:						Union[str, Any]		    =					model_class_name(snake_case__    )

            __a:						int		    =					jnp.concatenate(
                [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])    )]						, axis=-1						, )

            __a:						Dict		    =					model.init_cache(input_ids.shape[0]						, snake_case__    )
            __a:						Tuple		    =					jnp.broadcast_to(
                jnp.arange(input_ids.shape[-1] - 1    )[None, :]						, (input_ids.shape[0], input_ids.shape[-1] - 1)    )

            __a:						str		    =					model(
                input_ids[:, :-1]						, attention_mask=snake_case__						, past_key_values=snake_case__						, position_ids=snake_case__						, )
            __a:						List[Any]		    =					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						, dtype="""i4"""    )
            __a:						str		    =					model(
                input_ids[:, -1:]						, past_key_values=outputs_cache.past_key_values						, attention_mask=snake_case__						, position_ids=snake_case__						, )

            __a:						Any		    =					model(snake_case__						, attention_mask=snake_case__    )
            __a:						int		    =					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])    )    )
            self.parent.assertTrue(diff < 1e-3						, msg=f"""Max diff is {diff}"""    )






@require_sentencepiece
@require_flax
class    _snake_case  (						_lowercase ,				_lowercase ,				unittest.TestCase       ):
       SCREAMING_SNAKE_CASE		     =						(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
       SCREAMING_SNAKE_CASE		     =						(FlaxXGLMForCausalLM,) if is_flax_available() else ()






       def 							__lowercase  (					self						:			List[str]    ):






            """simple docstring"""



            __a:						Any		    =					FlaxXGLMModelTester(self    )






       def 							__lowercase  (					self						:			Union[str, Any]    ):






            """simple docstring"""



            for model_class_name in self.all_model_classes:
                 __a:						int		    =					self.model_tester.prepare_config_and_inputs()
                 self.model_tester.check_use_cache_forward(snake_case__						, snake_case__						, snake_case__						, snake_case__    )






       def 							__lowercase  (					self						:			int    ):






            """simple docstring"""



            for model_class_name in self.all_model_classes:
                 __a:						Any		    =					self.model_tester.prepare_config_and_inputs()
                 self.model_tester.check_use_cache_forward_with_attn_mask(
                     snake_case__						, snake_case__						, snake_case__						, snake_case__    )






       @slow
       def 							__lowercase  (					self						:			Optional[Any]    ):






            """simple docstring"""



            __a:						Tuple		    =					XGLMTokenizer.from_pretrained("""XGLM"""						, padding_side="""left"""    )
            __a:						Optional[int]		    =					tokenizer(["""Hello this is a long string""", """Hey"""]						, return_tensors="""np"""						, padding=snake_case__						, truncation=snake_case__    )

            __a:						str		    =					FlaxXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""    )
            __a:						Union[str, Any]		    =					1
            __a:						Dict		    =					False

            __a:						Optional[Any]		    =					jax.jit(model.generate    )

            __a:						List[str]		    =					jit_generate(inputs["""input_ids"""]						, attention_mask=inputs["""attention_mask"""]    ).sequences

            __a:						List[str]		    =					tokenizer.batch_decode(snake_case__						, skip_special_tokens=snake_case__    )

            __a:						Dict		    =					[
                """Hello this is a long string of questions, but I'm not sure if I'm""",
                """Hey, I'm a newbie to the forum and I'""",
            ]

            self.assertListEqual(snake_case__						, snake_case__    )






       @is_pt_flax_cross_test
       def 							__lowercase  (					self						:			List[str]    ):






            """simple docstring"""



            __a:						Any		    =					self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__    ):
                      # prepare inputs
                      __a:						List[str]		    =					self._prepare_for_class(snake_case__						, snake_case__    )
                      __a:						int		    =					{k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

                      # load corresponding PyTorch class
                      __a:						Any		    =					model_class.__name__[4:]  # Skip the "Flax" at the beginning
                      __a:						Dict		    =					getattr(snake_case__						, snake_case__    )

                      __a:						Dict		    =					pt_inputs["""input_ids"""].shape
                      __a:						Any		    =					np.random.randint(0						, seq_length - 1						, size=(batch_size,)    )
                      for batch_idx, start_index in enumerate(snake_case__    ):
                           __a:						Dict		    =					0
                           __a:						int		    =					1
                           __a:						List[Any]		    =					0
                           __a:						int		    =					1
                      __a:						Optional[int]		    =					pt_model_class(snake_case__    ).eval()
                      # Flax models don't use the `use_cache` option and cache is not returned as a default.
                      # So we disable `use_cache` here for PyTorch model.
                      __a:						Optional[Any]		    =					False
                      __a:						Dict		    =					model_class(snake_case__						, dtype=jnp.floataa    )
                      __a:						int		    =					convert_pytorch_state_dict_to_flax(pt_model.state_dict()						, snake_case__    )
                      __a:						Tuple		    =					fx_state

                      with torch.no_grad():
                           __a:						Union[str, Any]		    =					pt_model(**snake_case__    ).to_tuple()

                      __a:						str		    =					fx_model(**snake_case__    ).to_tuple()
                      self.assertEqual(len(snake_case__    )						, len(snake_case__    )						, """Output lengths differ between Flax and PyTorch"""    )
                      for fx_output, pt_output in zip(snake_case__						, snake_case__    ):
                           self.assert_almost_equals(fx_output[:, -1]						, pt_output[:, -1].numpy()						, 4e-2    )

                      with tempfile.TemporaryDirectory() as tmpdirname:
                           pt_model.save_pretrained(snake_case__    )
                           __a:						List[str]		    =					model_class.from_pretrained(snake_case__						, from_pt=snake_case__    )

                      __a:						List[Any]		    =					fx_model_loaded(**snake_case__    ).to_tuple()
                      self.assertEqual(
                          len(snake_case__    )						, len(snake_case__    )						, """Output lengths differ between Flax and PyTorch"""    )
                      for fx_output_loaded, pt_output in zip(snake_case__						, snake_case__    ):
                           self.assert_almost_equals(fx_output_loaded[:, -1]						, pt_output[:, -1].numpy()						, 4e-2    )






       @is_pt_flax_cross_test
       def 							__lowercase  (					self						:			Union[str, Any]    ):






            """simple docstring"""



            __a:						str		    =					self.model_tester.prepare_config_and_inputs_for_common()
            for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__    ):
                      # prepare inputs
                      __a:						Any		    =					self._prepare_for_class(snake_case__						, snake_case__    )
                      __a:						int		    =					{k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

                      # load corresponding PyTorch class
                      __a:						Optional[int]		    =					model_class.__name__[4:]  # Skip the "Flax" at the beginning
                      __a:						Optional[int]		    =					getattr(snake_case__						, snake_case__    )

                      __a:						Union[str, Any]		    =					pt_model_class(snake_case__    ).eval()
                      __a:						Any		    =					False
                      __a:						List[str]		    =					model_class(snake_case__						, dtype=jnp.floataa    )

                      __a:						str		    =					load_flax_weights_in_pytorch_model(snake_case__						, fx_model.params    )
                      __a:						Dict		    =					pt_inputs["""input_ids"""].shape
                      __a:						List[Any]		    =					np.random.randint(0						, seq_length - 1						, size=(batch_size,)    )
                      for batch_idx, start_index in enumerate(snake_case__    ):
                           __a:						int		    =					0
                           __a:						List[Any]		    =					1
                           __a:						Optional[int]		    =					0
                           __a:						Tuple		    =					1

                      # make sure weights are tied in PyTorch
                      pt_model.tie_weights()

                      with torch.no_grad():
                           __a:						Any		    =					pt_model(**snake_case__    ).to_tuple()

                      __a:						Optional[int]		    =					fx_model(**snake_case__    ).to_tuple()
                      self.assertEqual(len(snake_case__    )						, len(snake_case__    )						, """Output lengths differ between Flax and PyTorch"""    )
                      for fx_output, pt_output in zip(snake_case__						, snake_case__    ):
                           self.assert_almost_equals(fx_output[:, -1]						, pt_output[:, -1].numpy()						, 4e-2    )

                      with tempfile.TemporaryDirectory() as tmpdirname:
                           fx_model.save_pretrained(snake_case__    )
                           __a:						Any		    =					pt_model_class.from_pretrained(snake_case__						, from_flax=snake_case__    )

                      with torch.no_grad():
                           __a:						Dict		    =					pt_model_loaded(**snake_case__    ).to_tuple()

                      self.assertEqual(
                          len(snake_case__    )						, len(snake_case__    )						, """Output lengths differ between Flax and PyTorch"""    )
                      for fx_output, pt_output in zip(snake_case__						, snake_case__    ):
                           self.assert_almost_equals(fx_output[:, -1]						, pt_output[:, -1].numpy()						, 4e-2    )






       @slow
       def 							__lowercase  (					self						:			Optional[Any]    ):






            """simple docstring"""



            for model_class_name in self.all_model_classes:
                 __a:						List[str]		    =					model_class_name.from_pretrained("""facebook/xglm-564M"""    )
                 __a:						str		    =					model(np.ones((1, 1)    )    )
                 self.assertIsNotNone(snake_case__    )


"""simple docstring"""
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowercase		:						Tuple =					logging.get_logger(__name__)




class    _snake_case  (						_lowercase       ):
							SCREAMING_SNAKE_CASE		     =						['''pixel_values''']






							def __init__(					self						:			Union[str, Any]						, snake_case__						:			bool = True						, snake_case__						:			Optional[Dict[str, int]] = None						, snake_case__						:			PILImageResampling = PILImageResampling.BILINEAR						, snake_case__						:			bool = True						, snake_case__						:			Union[int, float] = 1 / 2_5_5						, snake_case__						:			bool = True						, snake_case__						:			Optional[Union[float, List[float]]] = None						, snake_case__						:			Optional[Union[float, List[float]]] = None						, **snake_case__						:			Optional[int]						, ):






												"""simple docstring"""



												super().__init__(**snake_case__    )
												__a:						Dict		    =					size if size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
												__a:						Optional[Any]		    =					get_size_dict(snake_case__    )
												__a:						Union[str, Any]		    =					do_resize
												__a:						str		    =					do_rescale
												__a:						Tuple		    =					do_normalize
												__a:						Dict		    =					size
												__a:						str		    =					resample
												__a:						Any		    =					rescale_factor
												__a:						Optional[int]		    =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												__a:						int		    =					image_std if image_std is not None else IMAGENET_STANDARD_STD






							def 							__lowercase  (					self						:			Optional[Any]						, snake_case__						:			np.ndarray						, snake_case__						:			Dict[str, int]						, snake_case__						:			PILImageResampling = PILImageResampling.BILINEAR						, snake_case__						:			Optional[Union[str, ChannelDimension]] = None						, **snake_case__						:			List[str]						, ):






												"""simple docstring"""



												__a:						int		    =					get_size_dict(snake_case__    )
												if "height" not in size or "width" not in size:
																	raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""    )
												return resize(
												    snake_case__						, size=(size["""height"""], size["""width"""])						, resample=snake_case__						, data_format=snake_case__						, **snake_case__    )






							def 							__lowercase  (					self						:			List[str]						, snake_case__						:			np.ndarray						, snake_case__						:			float						, snake_case__						:			Optional[Union[str, ChannelDimension]] = None						, **snake_case__						:			Union[str, Any]    ):






												"""simple docstring"""



												return rescale(snake_case__						, scale=snake_case__						, data_format=snake_case__						, **snake_case__    )






							def 							__lowercase  (					self						:			Dict						, snake_case__						:			np.ndarray						, snake_case__						:			Union[float, List[float]]						, snake_case__						:			Union[float, List[float]]						, snake_case__						:			Optional[Union[str, ChannelDimension]] = None						, **snake_case__						:			Union[str, Any]						, ):






												"""simple docstring"""



												return normalize(snake_case__						, mean=snake_case__						, std=snake_case__						, data_format=snake_case__						, **snake_case__    )






							def 							__lowercase  (					self						:			Optional[int]						, snake_case__						:			ImageInput						, snake_case__						:			Optional[bool] = None						, snake_case__						:			Dict[str, int] = None						, snake_case__						:			PILImageResampling = None						, snake_case__						:			Optional[bool] = None						, snake_case__						:			Optional[float] = None						, snake_case__						:			Optional[bool] = None						, snake_case__						:			Optional[Union[float, List[float]]] = None						, snake_case__						:			Optional[Union[float, List[float]]] = None						, snake_case__						:			Optional[Union[str, TensorType]] = None						, snake_case__						:			Union[str, ChannelDimension] = ChannelDimension.FIRST						, **snake_case__						:			List[str]						, ):






												"""simple docstring"""



												__a:						Any		    =					do_resize if do_resize is not None else self.do_resize
												__a:						Any		    =					do_rescale if do_rescale is not None else self.do_rescale
												__a:						int		    =					do_normalize if do_normalize is not None else self.do_normalize
												__a:						Any		    =					resample if resample is not None else self.resample
												__a:						Any		    =					rescale_factor if rescale_factor is not None else self.rescale_factor
												__a:						Dict		    =					image_mean if image_mean is not None else self.image_mean
												__a:						List[str]		    =					image_std if image_std is not None else self.image_std

												__a:						int		    =					size if size is not None else self.size
												__a:						Any		    =					get_size_dict(snake_case__    )

												__a:						List[str]		    =					make_list_of_images(snake_case__    )

												if not valid_images(snake_case__    ):
																	raise ValueError(
																	    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
																	    """torch.Tensor, tf.Tensor or jax.ndarray."""    )

												if do_resize and size is None:
																	raise ValueError("""Size must be specified if do_resize is True."""    )

												if do_rescale and rescale_factor is None:
																	raise ValueError("""Rescale factor must be specified if do_rescale is True."""    )

												# All transformations expect numpy arrays.
												__a:						str		    =					[to_numpy_array(snake_case__    ) for image in images]

												if do_resize:
																	__a:						Dict		    =					[self.resize(image=snake_case__						, size=snake_case__						, resample=snake_case__    ) for image in images]

												if do_rescale:
																	__a:						str		    =					[self.rescale(image=snake_case__						, scale=snake_case__    ) for image in images]

												if do_normalize:
																	__a:						Dict		    =					[self.normalize(image=snake_case__						, mean=snake_case__						, std=snake_case__    ) for image in images]

												__a:						int		    =					[to_channel_dimension_format(snake_case__						, snake_case__    ) for image in images]

												__a:						str		    =					{"""pixel_values""": images}
												return BatchFeature(data=snake_case__						, tensor_type=snake_case__    )
