


'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
  from PIL import Image




class      lowerCamelCase_  (      __a ):
      lowerCAmelCase__							=	'dandelin/vilt-b32-finetuned-vqa'
      lowerCAmelCase__							=	(
          'This is a tool that answers a question about an image. It takes an input named `image` which should be the '
          'image containing the information, as well as a `question` which should be the question in English. It '
          'returns a text that is the answer to the question.'
      )
      lowerCAmelCase__							=	'image_qa'
      lowerCAmelCase__							=	AutoProcessor
      lowerCAmelCase__							=	AutoModelForVisualQuestionAnswering

      lowerCAmelCase__							=	['image', 'text']
      lowerCAmelCase__							=	['text']






      def __init__(   self							:       Dict		,		*_A							:       int		,		**_A							:       Tuple					):


          '''simple docstring'''


          requires_backends(self		,		['''vision''']					)
          super().__init__(*_A		,		**_A					)






      def lowercase_				(   self							:       str		,		_A							:       "Image"		,		_A							:       str					):


          '''simple docstring'''


          return self.pre_processor(_A		,		_A		,		return_tensors='''pt'''					)






      def lowercase_				(   self							:       List[str]		,		_A							:       Optional[Any]					):


          '''simple docstring'''


          with torch.no_grad():
              return self.model(**_A					).logits






      def lowercase_				(   self							:       int		,		_A							:       Dict					):


          '''simple docstring'''


          UpperCAmelCase__     :						Any						=							outputs.argmax(-1					).item()
          return self.model.config.idalabel[idx]







import unittest

from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      FalconForCausalLM,
      FalconForQuestionAnswering,
      FalconForSequenceClassification,
      FalconForTokenClassification,
      FalconModel,
  )



class 					__snake_case	:


    '''simple docstring'''







    def __init__(       self   ,				A_   ,				A_=3   ,				A_=7   ,				A_=True   ,				A_=True   ,				A_=False   ,				A_=True   ,				A_=99   ,				A_=32   ,				A_=5   ,				A_=4   ,				A_=37   ,				A_="gelu"   ,				A_=0.1   ,				A_=0.1   ,				A_=5_12   ,				A_=16   ,				A_=2   ,				A_=0.02   ,				A_=3   ,				A_=4   ,				A_=None   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			parent
        SCREAMING_SNAKE_CASE__											=			batch_size
        SCREAMING_SNAKE_CASE__											=			seq_length
        SCREAMING_SNAKE_CASE__											=			is_training
        SCREAMING_SNAKE_CASE__											=			use_input_mask
        SCREAMING_SNAKE_CASE__											=			use_token_type_ids
        SCREAMING_SNAKE_CASE__											=			use_labels
        SCREAMING_SNAKE_CASE__											=			vocab_size
        SCREAMING_SNAKE_CASE__											=			hidden_size
        SCREAMING_SNAKE_CASE__											=			num_hidden_layers
        SCREAMING_SNAKE_CASE__											=			num_attention_heads
        SCREAMING_SNAKE_CASE__											=			intermediate_size
        SCREAMING_SNAKE_CASE__											=			hidden_act
        SCREAMING_SNAKE_CASE__											=			hidden_dropout_prob
        SCREAMING_SNAKE_CASE__											=			attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE__											=			max_position_embeddings
        SCREAMING_SNAKE_CASE__											=			type_vocab_size
        SCREAMING_SNAKE_CASE__											=			type_sequence_label_size
        SCREAMING_SNAKE_CASE__											=			initializer_range
        SCREAMING_SNAKE_CASE__											=			num_labels
        SCREAMING_SNAKE_CASE__											=			num_choices
        SCREAMING_SNAKE_CASE__											=			scope



    def  lowercase_					(       self     ):





        '''simple docstring'''

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

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

        SCREAMING_SNAKE_CASE__											=			None

        SCREAMING_SNAKE_CASE__											=			None
        SCREAMING_SNAKE_CASE__											=			None
        SCREAMING_SNAKE_CASE__											=			None
        if self.use_labels:
            SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size]   ,				self.type_sequence_label_size     )
            SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size, self.seq_length]   ,				self.num_labels     )
            SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size]   ,				self.num_choices     )

        SCREAMING_SNAKE_CASE__											=			self.get_config()

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



    def  lowercase_					(       self     ):





        '''simple docstring'''

        return FalconConfig(
            vocab_size=self.vocab_size   ,				hidden_size=self.hidden_size   ,				num_hidden_layers=self.num_hidden_layers   ,				num_attention_heads=self.num_attention_heads   ,				intermediate_size=self.intermediate_size   ,				hidden_act=self.hidden_act   ,				hidden_dropout_prob=self.hidden_dropout_prob   ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,				max_position_embeddings=self.max_position_embeddings   ,				type_vocab_size=self.type_vocab_size   ,				is_decoder=A_   ,				initializer_range=self.initializer_range   ,				pad_token_id=1   ,				new_decoder_architecture=A_   ,				)



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			FalconModel(config=A_     )
        model.to(A_     )
        model.eval()
        SCREAMING_SNAKE_CASE__											=			model(A_   ,				attention_mask=A_     )
        SCREAMING_SNAKE_CASE__											=			model(A_     )
        self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)     )



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			True
        SCREAMING_SNAKE_CASE__											=			FalconModel(A_     )
        model.to(A_     )
        model.eval()
        SCREAMING_SNAKE_CASE__											=			model(
            A_   ,				attention_mask=A_   ,				encoder_hidden_states=A_   ,				encoder_attention_mask=A_   ,				)
        SCREAMING_SNAKE_CASE__											=			model(
            A_   ,				attention_mask=A_   ,				encoder_hidden_states=A_   ,				)
        SCREAMING_SNAKE_CASE__											=			model(A_   ,				attention_mask=A_     )
        self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)     )



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			FalconForCausalLM(config=A_     )
        model.to(A_     )
        model.eval()
        SCREAMING_SNAKE_CASE__											=			model(A_   ,				attention_mask=A_   ,				labels=A_     )
        self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.seq_length, self.vocab_size)     )



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			True
        SCREAMING_SNAKE_CASE__											=			True
        SCREAMING_SNAKE_CASE__											=			FalconForCausalLM(config=A_     )
        model.to(A_     )
        model.eval()

        # first forward pass
        SCREAMING_SNAKE_CASE__											=			model(
            A_   ,				attention_mask=A_   ,				encoder_hidden_states=A_   ,				encoder_attention_mask=A_   ,				use_cache=A_   ,				)
        SCREAMING_SNAKE_CASE__											=			outputs.past_key_values

        # create hypothetical multiple next token and extent to next_input_ids
        SCREAMING_SNAKE_CASE__											=			ids_tensor((self.batch_size, 3)   ,				config.vocab_size     )
        SCREAMING_SNAKE_CASE__											=			ids_tensor((self.batch_size, 3)   ,				vocab_size=2     )

        # append to next input_ids and
        SCREAMING_SNAKE_CASE__											=			torch.cat([input_ids, next_tokens]   ,				dim=-1     )
        SCREAMING_SNAKE_CASE__											=			torch.cat([input_mask, next_mask]   ,				dim=-1     )

        SCREAMING_SNAKE_CASE__											=			model(
            A_   ,				attention_mask=A_   ,				encoder_hidden_states=A_   ,				encoder_attention_mask=A_   ,				output_hidden_states=A_   ,				)['''hidden_states'''][0]
        SCREAMING_SNAKE_CASE__											=			model(
            A_   ,				attention_mask=A_   ,				encoder_hidden_states=A_   ,				encoder_attention_mask=A_   ,				past_key_values=A_   ,				output_hidden_states=A_   ,				)['''hidden_states'''][0]

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

        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]     )

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(A_   ,				A_   ,				atol=1E-3     )     )




    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.prepare_config_and_inputs()
        (
            (
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		
        )											=			config_and_inputs
        SCREAMING_SNAKE_CASE__											=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
        return config, inputs_dict





@require_torch
class 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		Dict				=	(
        (
            FalconModel,
            FalconForCausalLM,
            FalconForSequenceClassification,
            FalconForTokenClassification,
            FalconForQuestionAnswering,
        )
        if is_torch_available()
        else ()
    )
    lowerCamelCase__    :		Dict				=	(FalconForCausalLM,) if is_torch_available() else ()
    lowerCamelCase__    :		List[Any]				=	(
        {
            """feature-extraction""": FalconModel,
            """text-classification""": FalconForSequenceClassification,
            """text-generation""": FalconForCausalLM,
            """question-answering""": FalconForQuestionAnswering,
            """token-classification""": FalconForTokenClassification,
            """zero-shot""": FalconForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    lowerCamelCase__    :		List[Any]				=	False
    lowerCamelCase__    :		Tuple				=	False



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			FalconModelTester(self     )
        SCREAMING_SNAKE_CASE__											=			ConfigTester(self   ,				config_class=A_   ,				hidden_size=37     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        self.config_tester.run_common_tests()



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*A_     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__				,		*SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs()
        for alibi in [True, False]:
            SCREAMING_SNAKE_CASE__											=			alibi
            self.model_tester.create_and_check_model(A_   ,				*A_     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs_for_common()
        SCREAMING_SNAKE_CASE__											=			3
        SCREAMING_SNAKE_CASE__											=			input_dict['''input_ids''']
        SCREAMING_SNAKE_CASE__											=			input_ids.ne(1     ).to(A_     )
        SCREAMING_SNAKE_CASE__											=			ids_tensor([self.model_tester.batch_size]   ,				self.model_tester.type_sequence_label_size     )
        SCREAMING_SNAKE_CASE__											=			FalconForSequenceClassification(A_     )
        model.to(A_     )
        model.eval()
        SCREAMING_SNAKE_CASE__											=			model(A_   ,				attention_mask=A_   ,				labels=A_     )
        self.assertEqual(result.logits.shape   ,				(self.model_tester.batch_size, self.model_tester.num_labels)     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs_for_common()
        SCREAMING_SNAKE_CASE__											=			3
        SCREAMING_SNAKE_CASE__											=			'''single_label_classification'''
        SCREAMING_SNAKE_CASE__											=			input_dict['''input_ids''']
        SCREAMING_SNAKE_CASE__											=			input_ids.ne(1     ).to(A_     )
        SCREAMING_SNAKE_CASE__											=			ids_tensor([self.model_tester.batch_size]   ,				self.model_tester.type_sequence_label_size     )
        SCREAMING_SNAKE_CASE__											=			FalconForSequenceClassification(A_     )
        model.to(A_     )
        model.eval()
        SCREAMING_SNAKE_CASE__											=			model(A_   ,				attention_mask=A_   ,				labels=A_     )
        self.assertEqual(result.logits.shape   ,				(self.model_tester.batch_size, self.model_tester.num_labels)     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs_for_common()
        SCREAMING_SNAKE_CASE__											=			input_dict['''input_ids''']
        SCREAMING_SNAKE_CASE__											=			FalconForCausalLM(A_     )
        model.to(A_     )
        model.eval()
        SCREAMING_SNAKE_CASE__											=			model(A_   ,				use_cache=A_     )
        SCREAMING_SNAKE_CASE__											=			input_ids.shape[0]
        SCREAMING_SNAKE_CASE__											=			model._convert_to_rw_cache(result.past_key_values     )
        SCREAMING_SNAKE_CASE__											=			model._convert_cache_to_standard_format(A_   ,				A_     )
        for layer in range(len(A_     )     ):
            for tensor_idx in range(2     ):
                self.assertTrue(rw_cache[layer][tensor_idx].ndim == 3     )
                self.assertTrue(result.past_key_values[layer][tensor_idx].ndim == 4     )
                self.assertTrue(
                    torch.all(result.past_key_values[layer][tensor_idx] == standard_cache[layer][tensor_idx]     )     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs_for_common()
        SCREAMING_SNAKE_CASE__											=			3
        SCREAMING_SNAKE_CASE__											=			'''multi_label_classification'''
        SCREAMING_SNAKE_CASE__											=			input_dict['''input_ids''']
        SCREAMING_SNAKE_CASE__											=			input_ids.ne(1     ).to(A_     )
        SCREAMING_SNAKE_CASE__											=			ids_tensor(
            [self.model_tester.batch_size, config.num_labels]   ,				self.model_tester.type_sequence_label_size     ).to(torch.float     )
        SCREAMING_SNAKE_CASE__											=			FalconForSequenceClassification(A_     )
        model.to(A_     )
        model.eval()
        SCREAMING_SNAKE_CASE__											=			model(A_   ,				attention_mask=A_   ,				labels=A_     )
        self.assertEqual(result.logits.shape   ,				(self.model_tester.batch_size, self.model_tester.num_labels)     )




    def  lowercase_					(       self     ):





        '''simple docstring'''

        for model_class in self.all_generative_model_classes:
            SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs_for_common()

            # If it doesn't support cache, pass the test
            if not hasattr(A_   ,				'''use_cache'''     ):
                return

            SCREAMING_SNAKE_CASE__											=			model_class(A_     ).to(A_     )
            if "use_cache" not in inputs:
                SCREAMING_SNAKE_CASE__											=			True
            SCREAMING_SNAKE_CASE__											=			model(**A_     )

            # If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
            if "past_key_values" not in outputs:
                return

            SCREAMING_SNAKE_CASE__											=			(
                getattr(A_   ,				'''decoder_layers'''   ,				A_     )
                or getattr(A_   ,				'''num_decoder_layers'''   ,				A_     )
                or config.num_hidden_layers
            )
            SCREAMING_SNAKE_CASE__											=			getattr(A_   ,				'''num_kv_heads'''   ,				config.num_attention_heads     )
            SCREAMING_SNAKE_CASE__											=			getattr(A_   ,				'''d_model'''   ,				config.hidden_size     )
            SCREAMING_SNAKE_CASE__											=			embed_dim // num_attention_heads

            SCREAMING_SNAKE_CASE__											=			outputs['''past_key_values''']
            self.assertEqual(len(A_     )   ,				A_     )

            SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			inputs['''input_ids'''].shape
            for i in range(A_     ):
                if config.new_decoder_architecture:
                    SCREAMING_SNAKE_CASE__											=			config.num_attention_heads
                elif config.multi_query:
                    SCREAMING_SNAKE_CASE__											=			1
                self.assertEqual(len(past_kv[0]     )   ,				2     )  # K V for the decoder = 2
                self.assertEqual(
                    past_kv[i][0].shape   ,				(batch_size, num_attention_heads, seq_length, per_head_embed_dim)     )
                self.assertEqual(
                    past_kv[i][1].shape   ,				(batch_size, num_attention_heads, seq_length, per_head_embed_dim)     )





@require_torch
class 					__snake_case	(  unittest.TestCase    ):


    '''simple docstring'''







    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			AutoTokenizer.from_pretrained('''Rocketknight1/falcon-rw-1b'''     )
        SCREAMING_SNAKE_CASE__											=			FalconForCausalLM.from_pretrained('''Rocketknight1/falcon-rw-1b'''     )
        model.eval()
        model.to(A_     )
        SCREAMING_SNAKE_CASE__											=			tokenizer('''My favorite food is'''   ,				return_tensors='''pt'''     ).to(A_     )

        SCREAMING_SNAKE_CASE__											=			(
            '''My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday.'''
        )

        SCREAMING_SNAKE_CASE__											=			model.generate(**A_   ,				do_sample=A_   ,				max_new_tokens=19     )
        SCREAMING_SNAKE_CASE__											=			tokenizer.batch_decode(A_     )[0]

        self.assertEqual(A_   ,				A_     )



    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

        for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]:
            SCREAMING_SNAKE_CASE__											=			AutoTokenizer.from_pretrained(A_     )
            SCREAMING_SNAKE_CASE__											=			FalconForCausalLM.from_pretrained(A_     )
            model.eval()
            model.to(A_     )
            SCREAMING_SNAKE_CASE__											=			tokenizer('''My favorite food is'''   ,				return_tensors='''pt'''     ).to(A_     )

            # We just test that these run without errors - the models are randomly initialized
            # and so the actual text outputs will be garbage
            model.generate(**A_   ,				do_sample=A_   ,				max_new_tokens=4     )
            model.generate(**A_   ,				do_sample=A_   ,				max_new_tokens=4     )
            model.generate(**A_   ,				num_beams=2   ,				max_new_tokens=4     )




    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

        with torch.no_grad():
            for repo in [
                "Rocketknight1/falcon-rw-1b",
                "Rocketknight1/tiny-random-falcon-7b",
                "Rocketknight1/tiny-random-falcon-40b",
            ]:
                SCREAMING_SNAKE_CASE__											=			AutoTokenizer.from_pretrained(A_     )
                SCREAMING_SNAKE_CASE__											=			FalconForCausalLM.from_pretrained(A_     )
                model.eval()
                model.to(device=A_     )
                SCREAMING_SNAKE_CASE__											=			tokenizer('''My favorite food is'''   ,				return_tensors='''pt'''     ).to(A_     )

                # Test results are the same with and without cache
                SCREAMING_SNAKE_CASE__											=			model.generate(**A_   ,				do_sample=A_   ,				max_new_tokens=20   ,				use_cache=A_     )
                SCREAMING_SNAKE_CASE__											=			model.generate(**A_   ,				do_sample=A_   ,				max_new_tokens=20   ,				use_cache=A_     )
                self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0     )
