



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	, lowerCamelCase_	, lowerCamelCase_=14	, lowerCamelCase_=7	, lowerCamelCase_=True	, lowerCamelCase_=True	, lowerCamelCase_=True	, lowerCamelCase_=99	, lowerCamelCase_=32	, lowerCamelCase_=5	, lowerCamelCase_=4	, lowerCamelCase_=37	, lowerCamelCase_="gelu"	, lowerCamelCase_=0.1	, lowerCamelCase_=0.1	, lowerCamelCase_=512	, lowerCamelCase_=0.02	, lowerCamelCase_=None	, ):
						"""simple docstring"""

						a        =     parent
						a        =     batch_size
						a        =     seq_length
						a        =     is_training
						a        =     use_input_mask
						a        =     use_labels
						a        =     vocab_size
						a        =     d_model
						a        =     num_hidden_layers
						a        =     num_attention_heads
						a        =     ffn_dim
						a        =     activation_function
						a        =     activation_dropout
						a        =     attention_dropout
						a        =     max_position_embeddings
						a        =     initializer_range
						a        =     None
						a        =     0
						a        =     2
						a        =     1




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     np.clip(ids_tensor([self.batch_size, self.seq_length]	, self.vocab_size	)	, 3	, self.vocab_size	)

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

						a        =     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=lowerCamelCase_	, 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   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.prepare_config_and_inputs()
						a  ,       a  ,       a        =     config_and_inputs
						a        =     {"input_ids": input_ids, "attention_mask": attention_mask}
						return config, inputs_dict




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a  ,       a  ,       a        =     self.prepare_config_and_inputs()

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

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




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     20
						a        =     model_class_name(lowerCamelCase_	)

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

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

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

						a        =     model(lowerCamelCase_	)

						a        =     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   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     20
						a        =     model_class_name(lowerCamelCase_	)

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

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

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

						a        =     model(lowerCamelCase_	, attention_mask=lowerCamelCase_	)
						a        =     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"""
	__A					=							(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
	__A					=							(FlaxXGLMForCausalLM,) if is_flax_available() else ()




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     FlaxXGLMModelTester(self	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						for model_class_name in self.all_model_classes:
											a  ,       a  ,       a        =     self.model_tester.prepare_config_and_inputs()
											self.model_tester.check_use_cache_forward(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						for model_class_name in self.all_model_classes:
											a  ,       a  ,       a        =     self.model_tester.prepare_config_and_inputs()
											self.model_tester.check_use_cache_forward_with_attn_mask(
											    lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	)




	@slow
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     XGLMTokenizer.from_pretrained("XGLM"	, padding_side="left"	)
						a        =     tokenizer(["Hello this is a long string", "Hey"]	, return_tensors="np"	, padding=lowerCamelCase_	, truncation=lowerCamelCase_	)

						a        =     FlaxXGLMForCausalLM.from_pretrained("facebook/xglm-564M"	)
						a        =     1
						a        =     False

						a        =     jax.jit(model.generate	)

						a        =     jit_generate(inputs["input_ids"]	, attention_mask=inputs["attention_mask"]	).sequences

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

						a        =     [
						    "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(lowerCamelCase_	, lowerCamelCase_	)




	@is_pt_flax_cross_test
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a  ,       a        =     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        =     self._prepare_for_class(lowerCamelCase_	, lowerCamelCase_	)
																a        =     {k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

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

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

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

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

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

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




	@is_pt_flax_cross_test
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a  ,       a        =     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        =     self._prepare_for_class(lowerCamelCase_	, lowerCamelCase_	)
																a        =     {k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

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

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

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

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

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

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

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

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

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




	@slow
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						for model_class_name in self.all_model_classes:
											a        =     model_class_name.from_pretrained("facebook/xglm-564M"	)
											a        =     model(np.ones((1, 1)	)	)
											self.assertIsNotNone(lowerCamelCase_	)





import warnings

from ...utils import logging
from .image_processing_detr import DetrImageProcessor


_lowercase:				Optional[int]       =	logging.get_logger(__name__)



class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""




	def __init__(self	, *lowerCamelCase_	, **lowerCamelCase_	):
						"""simple docstring"""

						warnings.warn(
						    "The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
						    " Please use DetrImageProcessor instead."	, lowerCamelCase_	, )
						super().__init__(*lowerCamelCase_	, **lowerCamelCase_	)
