

'''simple docstring'''




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_						:

  '''simple docstring'''





  __lowerCAmelCase       :			List[str]     	= LEDConfig
  __lowerCAmelCase       :			str     	= {}
  __lowerCAmelCase       :			Union[str, Any]     	= "gelu"
  def __init__(  self ,			a_ ,			a_=1_3 ,			a_=7 ,			a_=True ,			a_=False ,			a_=9_9 ,			a_=3_2 ,			a_=2 ,			a_=4 ,			a_=3_7 ,			a_=0.1 ,			a_=0.1 ,			a_=2_0 ,			a_=2 ,			a_=1 ,			a_=0 ,			a_=4 ,			)				->					Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase    				=						parent
    UpperCAmelCase    				=						batch_size
    UpperCAmelCase    				=						seq_length
    UpperCAmelCase    				=						is_training
    UpperCAmelCase    				=						use_labels
    UpperCAmelCase    				=						vocab_size
    UpperCAmelCase    				=						hidden_size
    UpperCAmelCase    				=						num_hidden_layers
    UpperCAmelCase    				=						num_attention_heads
    UpperCAmelCase    				=						intermediate_size

    UpperCAmelCase    				=						hidden_dropout_prob
    UpperCAmelCase    				=						attention_probs_dropout_prob
    UpperCAmelCase    				=						max_position_embeddings
    UpperCAmelCase    				=						eos_token_id
    UpperCAmelCase    				=						pad_token_id
    UpperCAmelCase    				=						bos_token_id
    UpperCAmelCase    				=						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
    UpperCAmelCase    				=						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
    UpperCAmelCase    				=						(
        self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
    )
  def     snake_case_      (  self  )				->					Union[str, Any]:


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

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

    UpperCAmelCase    				=						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 ,			)
    UpperCAmelCase    				=						prepare_led_inputs_dict(a_ ,			a_ ,			a_  )
    UpperCAmelCase    				=						tf.concat(
        [tf.zeros_like(a_  )[:, :-1], tf.ones_like(a_  )[:, -1:]] ,			axis=-1 ,			)
    UpperCAmelCase    				=						global_attention_mask
    return config, inputs_dict





  def     snake_case_      (  self ,			a_ ,			a_  )				->					List[str]:


    """simple docstring"""
    UpperCAmelCase    				=						TFLEDModel(config=a_  ).get_decoder()
    UpperCAmelCase    				=						inputs_dict['input_ids']

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

    # first forward pass
    UpperCAmelCase    				=						model(a_ ,			attention_mask=a_ ,			use_cache=a_  )

    UpperCAmelCase				,   UpperCAmelCase    				=						outputs.to_tuple()

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

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

    UpperCAmelCase    				=						model(a_ ,			attention_mask=a_  )[0]
    UpperCAmelCase    				=						model(a_ ,			attention_mask=a_ ,			past_key_values=a_  )[0]

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

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

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




def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Optional[int]							, SCREAMING_SNAKE_CASE   : Union[str, Any]							, SCREAMING_SNAKE_CASE   : Any							, SCREAMING_SNAKE_CASE   : Union[str, Any]=None							, SCREAMING_SNAKE_CASE   : Any=None							, SCREAMING_SNAKE_CASE   : List[str]=None							, SCREAMING_SNAKE_CASE   : Dict=None							, ):
  if attention_mask is None:
    UpperCAmelCase    				=						tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE							, config.pad_token_id    )							, tf.inta    )
  if decoder_attention_mask is None:
    UpperCAmelCase    				=						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:
    UpperCAmelCase    				=						tf.ones((config.encoder_layers, config.encoder_attention_heads)    )
  if decoder_head_mask is None:
    UpperCAmelCase    				=						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			):

  '''simple docstring'''





  __lowerCAmelCase       :			str     	= (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
  __lowerCAmelCase       :			str     	= (TFLEDForConditionalGeneration,) if is_tf_available() else ()
  __lowerCAmelCase       :			int     	= (
      {
          "conversational": TFLEDForConditionalGeneration,
          "feature-extraction": TFLEDModel,
          "summarization": TFLEDForConditionalGeneration,
          "text2text-generation": TFLEDForConditionalGeneration,
          "translation": TFLEDForConditionalGeneration,
      }
      if is_tf_available()
      else {}
  )
  __lowerCAmelCase       :			List[Any]     	= True
  __lowerCAmelCase       :			str     	= False
  __lowerCAmelCase       :			List[Any]     	= False
  __lowerCAmelCase       :			List[str]     	= False
  def     snake_case_      (  self  )				->					Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase    				=						TFLEDModelTester(self  )
    UpperCAmelCase    				=						ConfigTester(self ,			config_class=a_  )
  def     snake_case_      (  self  )				->					str:


    """simple docstring"""
    self.config_tester.run_common_tests()
  def     snake_case_      (  self  )				->					Optional[int]:


    """simple docstring"""
    UpperCAmelCase    				=						self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.check_decoder_model_past_large_inputs(*a_  )
  def     snake_case_      (  self  )				->					Dict:


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

    UpperCAmelCase    				=						True
    UpperCAmelCase    				=						self.model_tester.seq_length
    UpperCAmelCase    				=						self.model_tester.encoder_seq_length

    def check_decoder_attentions_output(a_  ):
      UpperCAmelCase    				=						outputs.decoder_attentions
      self.assertEqual(len(a_  ) ,			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(a_  ):
      UpperCAmelCase    				=						[t.numpy() for t in outputs.encoder_attentions]
      UpperCAmelCase    				=						[t.numpy() for t in outputs.encoder_global_attentions]
      self.assertEqual(len(a_  ) ,			self.model_tester.num_hidden_layers  )
      self.assertEqual(len(a_  ) ,			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:
      UpperCAmelCase    				=						True
      UpperCAmelCase    				=						False
      UpperCAmelCase    				=						False
      UpperCAmelCase    				=						model_class(a_  )
      UpperCAmelCase    				=						model(self._prepare_for_class(a_ ,			a_  )  )
      UpperCAmelCase    				=						len(a_  )
      self.assertEqual(config.output_hidden_states ,			a_  )
      check_encoder_attentions_output(a_  )

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

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

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

      self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) ,			len(a_  )  )
      self.assertEqual(model.config.output_hidden_states ,			a_  )
      check_encoder_attentions_output(a_  )
  @unittest.skip('LED keeps using potentially symbolic tensors in conditionals and breaks tracing.'  )
  def     snake_case_      (  self  )				->					Dict:


    """simple docstring"""
    pass





  def     snake_case_      (  self  )				->					Optional[int]:


    """simple docstring"""
    # TODO: Head-masking not yet implement
    pass




def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : int    ):
  return tf.constant(SCREAMING_SNAKE_CASE							, dtype=tf.intaa    )


_a					:			Tuple							=    1E-4



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

  '''simple docstring'''





  def     snake_case_      (  self  )				->					List[str]:


    """simple docstring"""
    UpperCAmelCase    				=						TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384'  ).led

    # change to intended input here
    UpperCAmelCase    				=						_long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]  )
    UpperCAmelCase    				=						_long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]  )
    UpperCAmelCase    				=						prepare_led_inputs_dict(model.config ,			a_ ,			a_  )
    UpperCAmelCase    				=						model(**a_  )[0]
    UpperCAmelCase    				=						(1, 1_0_2_4, 7_6_8)
    self.assertEqual(output.shape ,			a_  )
    # change to expected output here
    UpperCAmelCase    				=						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] ,			a_ ,			atol=1E-3  )





  def     snake_case_      (  self  )				->					str:


    """simple docstring"""
    UpperCAmelCase    				=						TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384'  )

    # change to intended input here
    UpperCAmelCase    				=						_long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]  )
    UpperCAmelCase    				=						_long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]  )
    UpperCAmelCase    				=						prepare_led_inputs_dict(model.config ,			a_ ,			a_  )
    UpperCAmelCase    				=						model(**a_  )[0]
    UpperCAmelCase    				=						(1, 1_0_2_4, model.config.vocab_size)
    self.assertEqual(output.shape ,			a_  )
    # change to expected output here
    UpperCAmelCase    				=						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] ,			a_ ,			atol=1E-3 ,			rtol=1E-3  )





'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_a					:			Any							=    {
    'configuration_groupvit': [
        'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'GroupViTConfig',
        'GroupViTOnnxConfig',
        'GroupViTTextConfig',
        'GroupViTVisionConfig',
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a					:			Union[str, Any]							=    [
      'GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'GroupViTModel',
      'GroupViTPreTrainedModel',
      'GroupViTTextModel',
      'GroupViTVisionModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a					:			Tuple							=    [
      'TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFGroupViTModel',
      'TFGroupViTPreTrainedModel',
      'TFGroupViTTextModel',
      'TFGroupViTVisionModel',
  ]

if TYPE_CHECKING:
  from .configuration_groupvit import (
      GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      GroupViTConfig,
      GroupViTOnnxConfig,
      GroupViTTextConfig,
      GroupViTVisionConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_groupvit import (
        GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        GroupViTModel,
        GroupViTPreTrainedModel,
        GroupViTTextModel,
        GroupViTVisionModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_groupvit import (
        TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFGroupViTModel,
        TFGroupViTPreTrainedModel,
        TFGroupViTTextModel,
        TFGroupViTVisionModel,
    )

else:
  import sys

  _a					:			Union[str, Any]							=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


