from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A_      =      {"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_      =      ["XGLMTokenizer"]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_      =      ["XGLMTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_      =      [
						    "XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "XGLMForCausalLM",
						    "XGLMModel",
						    "XGLMPreTrainedModel",
						]


try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_      =      [
						    "FlaxXGLMForCausalLM",
						    "FlaxXGLMModel",
						    "FlaxXGLMPreTrainedModel",
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_      =      [
						    "TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFXGLMForCausalLM",
						    "TFXGLMModel",
						    "TFXGLMPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_xglm import XGLMTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_xglm_fast import XGLMTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_xglm import (
												    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFXGLMForCausalLM,
												    TFXGLMModel,
												    TFXGLMPreTrainedModel,
												)


else:
						import sys

						A_      =      _LazyModule(__name__, globals()["__file__"], _import_structure)






from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import TFLEDForConditionalGeneration, TFLEDModel






@require_tf
class      __lowercase					:
						lowercase					 =     LEDConfig
						lowercase					 =     {}
						lowercase					 =     'gelu'

						def __init__(	self				:      List[str]							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      Dict=13							,							__lowerCamelCase				:      Optional[Any]=7							,							__lowerCamelCase				:      Any=True							,							__lowerCamelCase				:      List[str]=False							,							__lowerCamelCase				:      Any=99							,							__lowerCamelCase				:      Any=32							,							__lowerCamelCase				:      str=2							,							__lowerCamelCase				:      Optional[Any]=4							,							__lowerCamelCase				:      Union[str, Any]=37							,							__lowerCamelCase				:      str=0.1							,							__lowerCamelCase				:      Any=0.1							,							__lowerCamelCase				:      Optional[int]=20							,							__lowerCamelCase				:      Optional[Any]=2							,							__lowerCamelCase				:      Any=1							,							__lowerCamelCase				:      Optional[int]=0							,							__lowerCamelCase				:      Any=4							,							)							->		str:


									'''simple docstring'''

									lowercase							=		parent
									lowercase							=		batch_size
									lowercase							=		seq_length
									lowercase							=		is_training
									lowercase							=		use_labels
									lowercase							=		vocab_size
									lowercase							=		hidden_size
									lowercase							=		num_hidden_layers
									lowercase							=		num_attention_heads
									lowercase							=		intermediate_size

									lowercase							=		hidden_dropout_prob
									lowercase							=		attention_probs_dropout_prob
									lowercase							=		max_position_embeddings
									lowercase							=		eos_token_id
									lowercase							=		pad_token_id
									lowercase							=		bos_token_id
									lowercase							=		attention_window

									# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
									# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
									# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
									# because its local attention only attends to `self.attention_window` and one before and one after
									lowercase							=		self.attention_window + 2

									# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
									# the `test_attention_outputs` and `test_hidden_states_output` tests
									lowercase							=		(
									    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
									)

						def        __a		(	self				:      Optional[int]   )							->		Tuple:


									'''simple docstring'''

									lowercase							=		ids_tensor([self.batch_size, self.seq_length - 1]							,							self.vocab_size   )
									lowercase							=		tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )							,							1   )
									lowercase							=		tf.concat([input_ids, eos_tensor]							,							axis=1   )

									lowercase							=		ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size   )

									lowercase							=		self.config_cls(
									    vocab_size=self.vocab_size							,							d_model=self.hidden_size							,							encoder_layers=self.num_hidden_layers							,							decoder_layers=self.num_hidden_layers							,							encoder_attention_heads=self.num_attention_heads							,							decoder_attention_heads=self.num_attention_heads							,							encoder_ffn_dim=self.intermediate_size							,							decoder_ffn_dim=self.intermediate_size							,							dropout=self.hidden_dropout_prob							,							attention_dropout=self.attention_probs_dropout_prob							,							max_position_embeddings=self.max_position_embeddings							,							eos_token_ids=[2]							,							bos_token_id=self.bos_token_id							,							pad_token_id=self.pad_token_id							,							decoder_start_token_id=self.pad_token_id							,							attention_window=self.attention_window							,							**self.config_updates							,							)
									lowercase							=		prepare_led_inputs_dict(__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase   )
									lowercase							=		tf.concat(
									    [tf.zeros_like(__lowerCamelCase   )[:, :-1], tf.ones_like(__lowerCamelCase   )[:, -1:]]							,							axis=-1							,							)
									lowercase							=		global_attention_mask
									return config, inputs_dict



						def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      Dict   )							->		Optional[Any]:


									'''simple docstring'''

									lowercase							=		TFLEDModel(config=__lowerCamelCase   ).get_decoder()
									lowercase							=		inputs_dict['''input_ids''']

									lowercase							=		input_ids[:1, :]
									lowercase							=		inputs_dict['''attention_mask'''][:1, :]
									lowercase							=		1

									# first forward pass
									lowercase							=		model(__lowerCamelCase							,							attention_mask=__lowerCamelCase							,							use_cache=__lowerCamelCase   )

									lowercase		,lowercase							=		outputs.to_tuple()

									# create hypothetical next token and extent to next_input_ids
									lowercase							=		ids_tensor((self.batch_size, 3)							,							config.vocab_size   )
									lowercase							=		tf.cast(ids_tensor((self.batch_size, 3)							,							2   )							,							tf.inta   )

									# append to next input_ids and
									lowercase							=		tf.concat([input_ids, next_tokens]							,							axis=-1   )
									lowercase							=		tf.concat([attention_mask, next_attn_mask]							,							axis=-1   )

									lowercase							=		model(__lowerCamelCase							,							attention_mask=__lowerCamelCase   )[0]
									lowercase							=		model(__lowerCamelCase							,							attention_mask=__lowerCamelCase							,							past_key_values=__lowerCamelCase   )[0]

									self.parent.assertEqual(next_tokens.shape[1]							,							output_from_past.shape[1]   )

									# select random slice
									lowercase							=		int(ids_tensor((1,)							,							output_from_past.shape[-1]   )   )
									lowercase							=		output_from_no_past[:, -3:, random_slice_idx]
									lowercase							=		output_from_past[:, :, random_slice_idx]

									# test that outputs are equal for slice
									tf.debugging.assert_near(__lowerCamelCase							,							__lowerCamelCase							,							rtol=1E-3   )






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase=None,		UpperCAmelCase=None,		UpperCAmelCase=None,		UpperCAmelCase=None,		)->       str:



			"""simple docstring"""







			if attention_mask is None:
						lowercase							=		tf.cast(tf.math.not_equal(UpperCAmelCase,		config.pad_token_id       ),		tf.inta       )
			if decoder_attention_mask is None:
						lowercase							=		tf.concat(
						    [
						        tf.ones(decoder_input_ids[:, :1].shape,		dtype=tf.inta       ),
						        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:],		config.pad_token_id       ),		tf.inta       ),
						    ],		axis=-1,		)
			if head_mask is None:
						lowercase							=		tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
			if decoder_head_mask is None:
						lowercase							=		tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
			return {
			    "input_ids": input_ids,
			    "attention_mask": attention_mask,
			    "decoder_input_ids": decoder_input_ids,
			    "decoder_attention_mask": decoder_attention_mask,
			    "head_mask": head_mask,
			    "decoder_head_mask": decoder_head_mask,
			}






@require_tf
class      __lowercase					(    _A       ,     _A       ,     unittest.TestCase    ):
						lowercase					 =     (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
						lowercase					 =     (TFLEDForConditionalGeneration,) if is_tf_available() else ()
						lowercase					 =     (
						    {
						        'conversational': TFLEDForConditionalGeneration,
						        'feature-extraction': TFLEDModel,
						        'summarization': TFLEDForConditionalGeneration,
						        'text2text-generation': TFLEDForConditionalGeneration,
						        'translation': TFLEDForConditionalGeneration,
						    }
						    if is_tf_available()
						    else {}
						)
						lowercase					 =     True
						lowercase					 =     False
						lowercase					 =     False
						lowercase					 =     False

						def        __a		(	self				:      Dict   )							->		int:


									'''simple docstring'''

									lowercase							=		TFLEDModelTester(self   )
									lowercase							=		ConfigTester(self							,							config_class=__lowerCamelCase   )

						def        __a		(	self				:      Optional[Any]   )							->		int:


									'''simple docstring'''

									self.config_tester.run_common_tests()

						def        __a		(	self				:      List[str]   )							->		Any:


									'''simple docstring'''

									lowercase							=		self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*__lowerCamelCase   )

						def        __a		(	self				:      List[Any]   )							->		Tuple:


									'''simple docstring'''

									lowercase		,lowercase							=		self.model_tester.prepare_config_and_inputs_for_common()
									lowercase							=		tf.zeros_like(inputs_dict['''attention_mask''']   )
									lowercase							=		2
									lowercase							=		tf.where(
									    tf.range(self.model_tester.seq_length   )[None, :] < num_global_attn_indices							,							1							,							inputs_dict['''global_attention_mask''']							,							)

									lowercase							=		True
									lowercase							=		self.model_tester.seq_length
									lowercase							=		self.model_tester.encoder_seq_length

									def check_decoder_attentions_output(__lowerCamelCase				:      int   ):
												lowercase							=		outputs.decoder_attentions
												self.assertEqual(len(__lowerCamelCase   )							,							self.model_tester.num_hidden_layers   )
												self.assertListEqual(
												    list(decoder_attentions[0].shape[-3:]   )							,							[self.model_tester.num_attention_heads, seq_length, seq_length]							,							)

									def check_encoder_attentions_output(__lowerCamelCase				:      Optional[Any]   ):
												lowercase							=		[t.numpy() for t in outputs.encoder_attentions]
												lowercase							=		[t.numpy() for t in outputs.encoder_global_attentions]
												self.assertEqual(len(__lowerCamelCase   )							,							self.model_tester.num_hidden_layers   )
												self.assertEqual(len(__lowerCamelCase   )							,							self.model_tester.num_hidden_layers   )
												self.assertListEqual(
												    list(attentions[0].shape[-3:]   )							,							[self.model_tester.num_attention_heads, seq_length, seq_length]							,							)
												self.assertListEqual(
												    list(global_attentions[0].shape[-3:]   )							,							[self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]							,							)

									for model_class in self.all_model_classes:
												lowercase							=		True
												lowercase							=		False
												lowercase							=		False
												lowercase							=		model_class(__lowerCamelCase   )
												lowercase							=		model(self._prepare_for_class(__lowerCamelCase							,							__lowerCamelCase   )   )
												lowercase							=		len(__lowerCamelCase   )
												self.assertEqual(config.output_hidden_states							,							__lowerCamelCase   )
												check_encoder_attentions_output(__lowerCamelCase   )

												if self.is_encoder_decoder:
															lowercase							=		model_class(__lowerCamelCase   )
															lowercase							=		model(self._prepare_for_class(__lowerCamelCase							,							__lowerCamelCase   )   )
															self.assertEqual(config.output_hidden_states							,							__lowerCamelCase   )
															check_decoder_attentions_output(__lowerCamelCase   )

												# Check that output attentions can also be changed via the config
												del inputs_dict["output_attentions"]
												lowercase							=		True
												lowercase							=		model_class(__lowerCamelCase   )
												lowercase							=		model(self._prepare_for_class(__lowerCamelCase							,							__lowerCamelCase   )   )
												self.assertEqual(config.output_hidden_states							,							__lowerCamelCase   )
												check_encoder_attentions_output(__lowerCamelCase   )

												# Check attention is always last and order is fine
												lowercase							=		True
												lowercase							=		True
												lowercase							=		model_class(__lowerCamelCase   )
												lowercase							=		model(self._prepare_for_class(__lowerCamelCase							,							__lowerCamelCase   )   )

												self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)							,							len(__lowerCamelCase   )   )
												self.assertEqual(model.config.output_hidden_states							,							__lowerCamelCase   )
												check_encoder_attentions_output(__lowerCamelCase   )

						@unittest.skip('''LED keeps using potentially symbolic tensors in conditionals and breaks tracing.'''   )
						def        __a		(	self				:      Dict   )							->		str:


									'''simple docstring'''

									pass



						def        __a		(	self				:      List[Any]   )							->		Optional[int]:


									'''simple docstring'''

									pass






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Optional[Any]:



			"""simple docstring"""







			return tf.constant(UpperCAmelCase,		dtype=tf.intaa       )


A_      =      1e-4






@slow
@require_tf
class      __lowercase					(    unittest.TestCase    ):

						def        __a		(	self				:      List[Any]   )							->		str:


									'''simple docstring'''

									lowercase							=		TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''   ).led

									# change to intended input here
									lowercase							=		_long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]   )
									lowercase							=		_long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]   )
									lowercase							=		prepare_led_inputs_dict(model.config							,							__lowerCamelCase							,							__lowerCamelCase   )
									lowercase							=		model(**__lowerCamelCase   )[0]
									lowercase							=		(1, 10_24, 7_68)
									self.assertEqual(output.shape							,							__lowerCamelCase   )
									# change to expected output here
									lowercase							=		tf.convert_to_tensor(
									    [[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]]							,							)
									tf.debugging.assert_near(output[:, :3, :3]							,							__lowerCamelCase							,							atol=1E-3   )



						def        __a		(	self				:      str   )							->		Optional[Any]:


									'''simple docstring'''

									lowercase							=		TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''   )

									# change to intended input here
									lowercase							=		_long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]   )
									lowercase							=		_long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]   )
									lowercase							=		prepare_led_inputs_dict(model.config							,							__lowerCamelCase							,							__lowerCamelCase   )
									lowercase							=		model(**__lowerCamelCase   )[0]
									lowercase							=		(1, 10_24, model.config.vocab_size)
									self.assertEqual(output.shape							,							__lowerCamelCase   )
									# change to expected output here
									lowercase							=		tf.convert_to_tensor(
									    [[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]]							,							)
									tf.debugging.assert_near(output[:, :3, :3]							,							__lowerCamelCase							,							atol=1E-3							,							rtol=1E-3   )





