



from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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 TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel



@require_tf
class     _UpperCAmelCase			:
     UpperCamelCase__		    =				PegasusConfig
     UpperCamelCase__		    =				{}
     UpperCamelCase__		    =				'''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__=4_0 ,						a__=2 ,						a__=1 ,						a__=0 ,						):
         A__		 =					parent
         A__		 =					batch_size
         A__		 =					seq_length
         A__		 =					is_training
         A__		 =					use_labels
         A__		 =					vocab_size
         A__		 =					hidden_size
         A__		 =					num_hidden_layers
         A__		 =					num_attention_heads
         A__		 =					intermediate_size

         A__		 =					hidden_dropout_prob
         A__		 =					attention_probs_dropout_prob
         A__		 =					max_position_embeddings
         A__		 =					eos_token_id
         A__		 =					pad_token_id
         A__		 =					bos_token_id




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

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

         A__		 =					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 ,						**self.config_updates ,						)
         A__		 =					prepare_pegasus_inputs_dict(a__ ,						a__ ,						a__)
         return config, inputs_dict




     def 		snake_case_		(				self ,						a__ ,						a__):
         A__		 =					TFPegasusModel(config=a__).get_decoder()
         A__		 =					inputs_dict['''input_ids''']

         A__		 =					input_ids[:1, :]
         A__		 =					inputs_dict['''attention_mask'''][:1, :]
         A__		 =					inputs_dict['''head_mask''']
         A__		 =					1

         # first forward pass
         A__		 =					model(a__ ,						attention_mask=a__ ,						head_mask=a__ ,						use_cache=a__)

         A__ ,	A__		 =					outputs.to_tuple()

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

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

         A__		 =					model(a__ ,						attention_mask=a__)[0]
         A__		 =					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
         A__		 =					int(ids_tensor((1,) ,						output_from_past.shape[-1]))
         A__		 =					output_from_no_past[:, -3:, random_slice_idx]
         A__		 =					output_from_past[:, :, random_slice_idx]

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







def 		lowerCAmelCase__    (					UpperCamelCase_				:       Dict ,       UpperCamelCase_				:       Any ,       UpperCamelCase_				:       Any ,       UpperCamelCase_				:       Any=None ,       UpperCamelCase_				:       Union[str, Any]=None ,       UpperCamelCase_				:       Optional[int]=None ,       UpperCamelCase_				:       Optional[int]=None ,       UpperCamelCase_				:       int=None ,       )->  Union[str, Any]:
    if attention_mask is None:
        A__		 =					tf.cast(tf.math.not_equal(UpperCamelCase_ ,       config.pad_token_id	) ,       tf.inta	)
    if decoder_attention_mask is None:
        A__		 =					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:
        A__		 =					tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
    if decoder_head_mask is None:
        A__		 =					tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
    if cross_attn_head_mask is None:
        A__		 =					tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": decoder_attention_mask,
        "head_mask": head_mask,
        "decoder_head_mask": decoder_head_mask,
        "cross_attn_head_mask": cross_attn_head_mask,
    }



@require_tf
class     _UpperCAmelCase			(       A__			,      A__			,      unittest.TestCase     ):
     UpperCamelCase__		    =				(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
     UpperCamelCase__		    =				(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
     UpperCamelCase__		    =				(
         {
             '''conversational''': TFPegasusForConditionalGeneration,
             '''feature-extraction''': TFPegasusModel,
             '''summarization''': TFPegasusForConditionalGeneration,
             '''text2text-generation''': TFPegasusForConditionalGeneration,
             '''translation''': TFPegasusForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     UpperCamelCase__		    =				True
     UpperCamelCase__		    =				False
     UpperCamelCase__		    =				False




     def 		snake_case_		(				self):
         A__		 =					TFPegasusModelTester(self)
         A__		 =					ConfigTester(self ,						config_class=a__)




     def 		snake_case_		(				self):
         self.config_tester.run_common_tests()




     def 		snake_case_		(				self):
         A__		 =					self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_decoder_model_past_large_inputs(*a__)







@require_sentencepiece
@require_tokenizers
@require_tf
class     _UpperCAmelCase			(       unittest.TestCase     ):
     UpperCamelCase__		    =				[
         ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
         ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
     ]
     UpperCamelCase__		    =				[
         '''California\'s largest electricity provider has cut power to hundreds of thousands of customers in an effort to'''
         ''' reduce the risk of wildfires.''',
         '''N-Dubz have revealed they\'re "grateful" to have been nominated for four Mobo Awards.''',
     ]  # differs slightly from pytorch, likely due to numerical differences in linear layers
     UpperCamelCase__		    =				'''google/pegasus-xsum'''




     @cached_property
     def 		snake_case_		(				self):
         return AutoTokenizer.from_pretrained(self.model_name)




     @cached_property
     def 		snake_case_		(				self):
         A__		 =					TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
         return model




     def 		snake_case_		(				self ,						**a__):
         A__		 =					self.translate_src_text(**a__)
         assert self.expected_text == generated_words




     def 		snake_case_		(				self ,						**a__):
         A__		 =					self.tokenizer(self.src_text ,						**a__ ,						padding=a__ ,						return_tensors='''tf''')
         A__		 =					self.model.generate(
             model_inputs.input_ids ,						attention_mask=model_inputs.attention_mask ,						num_beams=2 ,						use_cache=a__ ,						)
         A__		 =					self.tokenizer.batch_decode(generated_ids.numpy() ,						skip_special_tokens=a__)
         return generated_words




     @slow
     def 		snake_case_		(				self):
         self._assert_generated_batch_equal_expected()





import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image
else:



   class     _UpperCAmelCase			:




        @staticmethod
        def 		snake_case_		(				*a__ ,						**a__):
            pass



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Image	)->  str:
    A__		 =					hashlib.mda(image.tobytes()	)
    return m.hexdigest()



@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     _UpperCAmelCase			(       unittest.TestCase     ):
     UpperCamelCase__		    =				MODEL_FOR_DEPTH_ESTIMATION_MAPPING




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__):
         A__		 =					DepthEstimationPipeline(model=a__ ,						image_processor=a__)
         return depth_estimator, [
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
         ]




     def 		snake_case_		(				self ,						a__ ,						a__):
         A__		 =					depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
         self.assertEqual({'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)} ,						a__)
         import datasets

         A__		 =					datasets.load_dataset('''hf-internal-testing/fixtures_image_utils''' ,						'''image''' ,						split='''test''')
         A__		 =					depth_estimator(
             [
                 Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''),
                 '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                 # RGBA
                 dataset[0]['''file'''],
                 # LA
                 dataset[1]['''file'''],
                 # L
                 dataset[2]['''file'''],
             ])
         self.assertEqual(
             [
                 {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                 {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                 {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                 {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                 {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
             ] ,						a__ ,						)




     @require_tf
     @unittest.skip('''Depth estimation is not implemented in TF''')
     def 		snake_case_		(				self):
         pass




     @slow
     @require_torch
     def 		snake_case_		(				self):
         A__		 =					'''Intel/dpt-large'''
         A__		 =					pipeline('''depth-estimation''' ,						model=a__)
         A__		 =					depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg''')
         A__		 =					hashimage(outputs['''depth'''])

         # This seems flaky.
         # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
         self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item()) ,						2_9.3_0_4)
         self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()) ,						2.6_6_2)




     @require_torch
     def 		snake_case_		(				self):
         # This is highly irregular to have no small tests.
         self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT''')
