'''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   _lowercase:



   """simple docstring"""





   def __init__(							self:    str			,a:    int			,a:    Optional[Any]=14			,a:    Union[str, Any]=7			,a:    str=True			,a:    Optional[int]=True			,a:    Optional[Any]=True			,a:    List[Any]=99			,a:    Any=32			,a:    str=5			,a:    str=4			,a:    Optional[Any]=37			,a:    Any="gelu"			,a:    Dict=0.1			,a:    Tuple=0.1			,a:    Optional[int]=512			,a:    Optional[Any]=0.02			,a:    Tuple=None			,):
    __UpperCAmelCase         =					parent
    __UpperCAmelCase         =					batch_size
    __UpperCAmelCase         =					seq_length
    __UpperCAmelCase         =					is_training
    __UpperCAmelCase         =					use_input_mask
    __UpperCAmelCase         =					use_labels
    __UpperCAmelCase         =					vocab_size
    __UpperCAmelCase         =					d_model
    __UpperCAmelCase         =					num_hidden_layers
    __UpperCAmelCase         =					num_attention_heads
    __UpperCAmelCase         =					ffn_dim
    __UpperCAmelCase         =					activation_function
    __UpperCAmelCase         =					activation_dropout
    __UpperCAmelCase         =					attention_dropout
    __UpperCAmelCase         =					max_position_embeddings
    __UpperCAmelCase         =					initializer_range
    __UpperCAmelCase         =					None
    __UpperCAmelCase         =					0
    __UpperCAmelCase         =					2
    __UpperCAmelCase         =					1





   def 			snake_case	(							self:    Any	):
    __UpperCAmelCase         =					np.clip(ids_tensor([self.batch_size, self.seq_length]			,self.vocab_size	)			,3			,self.vocab_size	)

    __UpperCAmelCase         =					None
    if self.use_input_mask:
     __UpperCAmelCase         =					random_attention_mask([self.batch_size, self.seq_length]	)

    __UpperCAmelCase         =					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=a			,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 			snake_case	(							self:    List[str]	):
    __UpperCAmelCase         =					self.prepare_config_and_inputs()
    __UpperCAmelCase  ,			__UpperCAmelCase  ,			__UpperCAmelCase         =					config_and_inputs
    __UpperCAmelCase         =					{'input_ids': input_ids, 'attention_mask': attention_mask}
    return config, inputs_dict





   def 			snake_case	(							self:    Union[str, Any]	):
    __UpperCAmelCase  ,			__UpperCAmelCase  ,			__UpperCAmelCase         =					self.prepare_config_and_inputs()

    __UpperCAmelCase         =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
    __UpperCAmelCase         =					ids_tensor([self.batch_size, self.seq_length]			,vocab_size=2	)

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





   def 			snake_case	(							self:    Dict			,a:    List[str]			,a:    Tuple			,a:    Any			,a:    Dict	):
    __UpperCAmelCase         =					20
    __UpperCAmelCase         =					model_class_name(a	)

    __UpperCAmelCase         =					model.init_cache(input_ids.shape[0]			,a	)
    __UpperCAmelCase         =					jnp.ones((input_ids.shape[0], max_decoder_length)			,dtype='i4'	)

    __UpperCAmelCase         =					jnp.broadcast_to(
        jnp.arange(input_ids.shape[-1] - 1	)[None, :]			,(input_ids.shape[0], input_ids.shape[-1] - 1)	)
    __UpperCAmelCase         =					model(
        input_ids[:, :-1]			,attention_mask=a			,past_key_values=a			,position_ids=a			,)

    __UpperCAmelCase         =					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,dtype='i4'	)
    __UpperCAmelCase         =					model(
        input_ids[:, -1:]			,attention_mask=a			,past_key_values=outputs_cache.past_key_values			,position_ids=a			,)

    __UpperCAmelCase         =					model(a	)

    __UpperCAmelCase         =					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 			snake_case	(							self:    Tuple			,a:    str			,a:    Dict			,a:    Union[str, Any]			,a:    List[Any]	):
    __UpperCAmelCase         =					20
    __UpperCAmelCase         =					model_class_name(a	)

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

    __UpperCAmelCase         =					model.init_cache(input_ids.shape[0]			,a	)
    __UpperCAmelCase         =					jnp.broadcast_to(
        jnp.arange(input_ids.shape[-1] - 1	)[None, :]			,(input_ids.shape[0], input_ids.shape[-1] - 1)	)

    __UpperCAmelCase         =					model(
        input_ids[:, :-1]			,attention_mask=a			,past_key_values=a			,position_ids=a			,)
    __UpperCAmelCase         =					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,dtype='i4'	)
    __UpperCAmelCase         =					model(
        input_ids[:, -1:]			,past_key_values=outputs_cache.past_key_values			,attention_mask=a			,position_ids=a			,)

    __UpperCAmelCase         =					model(a			,attention_mask=a	)
    __UpperCAmelCase         =					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   _lowercase(     _lowerCamelCase   ,_lowerCamelCase   ,unittest.TestCase				):



   """simple docstring"""





   __lowerCamelCase    =  (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
   __lowerCamelCase    =  (FlaxXGLMForCausalLM,) if is_flax_available() else ()





   def 			snake_case	(							self:    Tuple	):
    __UpperCAmelCase         =					FlaxXGLMModelTester(self	)





   def 			snake_case	(							self:    Any	):
    for model_class_name in self.all_model_classes:
     __UpperCAmelCase  ,			__UpperCAmelCase  ,			__UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs()
     self.model_tester.check_use_cache_forward(a			,a			,a			,a	)





   def 			snake_case	(							self:    Any	):
    for model_class_name in self.all_model_classes:
     __UpperCAmelCase  ,			__UpperCAmelCase  ,			__UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs()
     self.model_tester.check_use_cache_forward_with_attn_mask(
         a			,a			,a			,a	)





   @slow
   def 			snake_case	(							self:    str	):
    __UpperCAmelCase         =					XGLMTokenizer.from_pretrained('XGLM'			,padding_side='left'	)
    __UpperCAmelCase         =					tokenizer(['Hello this is a long string', 'Hey']			,return_tensors='np'			,padding=a			,truncation=a	)

    __UpperCAmelCase         =					FlaxXGLMForCausalLM.from_pretrained('facebook/xglm-564M'	)
    __UpperCAmelCase         =					1
    __UpperCAmelCase         =					False

    __UpperCAmelCase         =					jax.jit(model.generate	)

    __UpperCAmelCase         =					jit_generate(inputs['input_ids']			,attention_mask=inputs['attention_mask']	).sequences

    __UpperCAmelCase         =					tokenizer.batch_decode(a			,skip_special_tokens=a	)

    __UpperCAmelCase         =					[
        '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(a			,a	)





   @is_pt_flax_cross_test
   def 			snake_case	(							self:    Dict	):
    __UpperCAmelCase  ,			__UpperCAmelCase         =					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
      __UpperCAmelCase         =					self._prepare_for_class(a			,a	)
      __UpperCAmelCase         =					{k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

      # load corresponding PyTorch class
      __UpperCAmelCase         =					model_class.__name__[4:]  # Skip the "Flax" at the beginning
      __UpperCAmelCase         =					getattr(a			,a	)

      __UpperCAmelCase  ,			__UpperCAmelCase         =					pt_inputs['input_ids'].shape
      __UpperCAmelCase         =					np.random.randint(0			,seq_length - 1			,size=(batch_size,)	)
      for batch_idx, start_index in enumerate(a	):
       __UpperCAmelCase         =					0
       __UpperCAmelCase         =					1
       __UpperCAmelCase         =					0
       __UpperCAmelCase         =					1
      __UpperCAmelCase         =					pt_model_class(a	).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.
      __UpperCAmelCase         =					False
      __UpperCAmelCase         =					model_class(a			,dtype=jnp.floataa	)
      __UpperCAmelCase         =					convert_pytorch_state_dict_to_flax(pt_model.state_dict()			,a	)
      __UpperCAmelCase         =					fx_state

      with torch.no_grad():
       __UpperCAmelCase         =					pt_model(**a	).to_tuple()

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

      with tempfile.TemporaryDirectory() as tmpdirname:
       pt_model.save_pretrained(a	)
       __UpperCAmelCase         =					model_class.from_pretrained(a			,from_pt=a	)

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





   @is_pt_flax_cross_test
   def 			snake_case	(							self:    Any	):
    __UpperCAmelCase  ,			__UpperCAmelCase         =					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
      __UpperCAmelCase         =					self._prepare_for_class(a			,a	)
      __UpperCAmelCase         =					{k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

      # load corresponding PyTorch class
      __UpperCAmelCase         =					model_class.__name__[4:]  # Skip the "Flax" at the beginning
      __UpperCAmelCase         =					getattr(a			,a	)

      __UpperCAmelCase         =					pt_model_class(a	).eval()
      __UpperCAmelCase         =					False
      __UpperCAmelCase         =					model_class(a			,dtype=jnp.floataa	)

      __UpperCAmelCase         =					load_flax_weights_in_pytorch_model(a			,fx_model.params	)
      __UpperCAmelCase  ,			__UpperCAmelCase         =					pt_inputs['input_ids'].shape
      __UpperCAmelCase         =					np.random.randint(0			,seq_length - 1			,size=(batch_size,)	)
      for batch_idx, start_index in enumerate(a	):
       __UpperCAmelCase         =					0
       __UpperCAmelCase         =					1
       __UpperCAmelCase         =					0
       __UpperCAmelCase         =					1

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

      with torch.no_grad():
       __UpperCAmelCase         =					pt_model(**a	).to_tuple()

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

      with tempfile.TemporaryDirectory() as tmpdirname:
       fx_model.save_pretrained(a	)
       __UpperCAmelCase         =					pt_model_class.from_pretrained(a			,from_flax=a	)

      with torch.no_grad():
       __UpperCAmelCase         =					pt_model_loaded(**a	).to_tuple()

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





   @slow
   def 			snake_case	(							self:    Any	):
    for model_class_name in self.all_model_classes:
     __UpperCAmelCase         =					model_class_name.from_pretrained('facebook/xglm-564M'	)
     __UpperCAmelCase         =					model(np.ones((1, 1)	)	)
     self.assertIsNotNone(a	)




'''simple docstring'''




import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
     from PIL import Image

class   _lowercase(     unittest.TestCase   ,_lowerCamelCase				):



   """simple docstring"""





   def 			snake_case	(							self:    Optional[int]	):
    __UpperCAmelCase         =					load_tool('image-captioning'	)
    self.tool.setup()
    __UpperCAmelCase         =					load_tool('image-captioning'			,remote=a	)





   def 			snake_case	(							self:    List[Any]	):
    __UpperCAmelCase         =					Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'	)	) / '000000039769.png'	)
    __UpperCAmelCase         =					self.tool(a	)
    self.assertEqual(a			,'two cats sleeping on a couch'	)





   def 			snake_case	(							self:    List[Any]	):
    __UpperCAmelCase         =					Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'	)	) / '000000039769.png'	)
    __UpperCAmelCase         =					self.remote_tool(a	)
    self.assertEqual(a			,'two cats sleeping on a couch'	)





   def 			snake_case	(							self:    Optional[Any]	):
    __UpperCAmelCase         =					Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'	)	) / '000000039769.png'	)
    __UpperCAmelCase         =					self.tool(image=a	)
    self.assertEqual(a			,'two cats sleeping on a couch'	)





   def 			snake_case	(							self:    Tuple	):
    __UpperCAmelCase         =					Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'	)	) / '000000039769.png'	)
    __UpperCAmelCase         =					self.remote_tool(image=a	)
    self.assertEqual(a			,'two cats sleeping on a couch'	)



