





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
_A					:				Any				=			logging.get_logger(__name__)


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_=False	)				->						List[str]:
    SCREAMING_SNAKE_CASE__											=			[]
    for i in range(config.num_hidden_layers	):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append((f'''blocks.{i}.norm1.weight''', f'''vit.encoder.layer.{i}.layernorm_before.weight''')	)
        rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''vit.encoder.layer.{i}.layernorm_before.bias''')	)
        rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''vit.encoder.layer.{i}.attention.output.dense.weight''')	)
        rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''vit.encoder.layer.{i}.attention.output.dense.bias''')	)
        rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''vit.encoder.layer.{i}.layernorm_after.weight''')	)
        rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''vit.encoder.layer.{i}.layernorm_after.bias''')	)
        rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''vit.encoder.layer.{i}.intermediate.dense.weight''')	)
        rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''vit.encoder.layer.{i}.intermediate.dense.bias''')	)
        rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''vit.encoder.layer.{i}.output.dense.weight''')	)
        rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''vit.encoder.layer.{i}.output.dense.bias''')	)

    # projection layer + position embeddings
    rename_keys.extend(
        [
            ('''cls_token''', '''vit.embeddings.cls_token'''),
            ('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
            ('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
            ('''pos_embed''', '''vit.embeddings.position_embeddings'''),
        ]	)

    if base_model:
        # layernorm + pooler
        rename_keys.extend(
            [
                ('''norm.weight''', '''layernorm.weight'''),
                ('''norm.bias''', '''layernorm.bias'''),
            ]	)

        # if just the base model, we should remove "vit" from all keys that start with "vit"
        SCREAMING_SNAKE_CASE__											=			[(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''	) else pair for pair in rename_keys]
    else:
        # layernorm + classification head
        rename_keys.extend(
            [
                ('''norm.weight''', '''vit.layernorm.weight'''),
                ('''norm.bias''', '''vit.layernorm.bias'''),
                ('''head.weight''', '''classifier.weight'''),
                ('''head.bias''', '''classifier.bias'''),
            ]	)

    return rename_keys


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_=False	)				->						Union[str, Any]:
    for i in range(config.num_hidden_layers	):
        if base_model:
            SCREAMING_SNAKE_CASE__											=			''''''
        else:
            SCREAMING_SNAKE_CASE__											=			'''vit.'''
        # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
        SCREAMING_SNAKE_CASE__											=			state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''	)
        SCREAMING_SNAKE_CASE__											=			state_dict.pop(f'''blocks.{i}.attn.qkv.bias'''	)
        # next, add query, keys and values (in that order) to the state dict
        SCREAMING_SNAKE_CASE__											=			in_proj_weight[
            : config.hidden_size, :
        ]
        SCREAMING_SNAKE_CASE__											=			in_proj_bias[: config.hidden_size]
        SCREAMING_SNAKE_CASE__											=			in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        SCREAMING_SNAKE_CASE__											=			in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        SCREAMING_SNAKE_CASE__											=			in_proj_weight[
            -config.hidden_size :, :
        ]
        SCREAMING_SNAKE_CASE__											=			in_proj_bias[-config.hidden_size :]


def 							__snake_case							(    lowerCAmelCase_	)				->						Any:
    SCREAMING_SNAKE_CASE__											=			['''head.weight''', '''head.bias''']
    for k in ignore_keys:
        state_dict.pop(lowerCAmelCase_ ,							lowerCAmelCase_	)


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Optional[Any]:
    SCREAMING_SNAKE_CASE__											=			dct.pop(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			val


def 							__snake_case							(    )				->						List[Any]:
    SCREAMING_SNAKE_CASE__											=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    SCREAMING_SNAKE_CASE__											=			Image.open(requests.get(lowerCAmelCase_ ,							stream=lowerCAmelCase_	).raw	)
    return im


@torch.no_grad()
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_=True	)				->						Dict:
    SCREAMING_SNAKE_CASE__											=			ViTConfig()
    # patch_size
    if model_name[-1] == "8":
        SCREAMING_SNAKE_CASE__											=			8
    # set labels if required
    if not base_model:
        SCREAMING_SNAKE_CASE__											=			1_0_0_0
        SCREAMING_SNAKE_CASE__											=			'''huggingface/label-files'''
        SCREAMING_SNAKE_CASE__											=			'''imagenet-1k-id2label.json'''
        SCREAMING_SNAKE_CASE__											=			json.load(open(hf_hub_download(lowerCAmelCase_ ,							lowerCAmelCase_ ,							repo_type='''dataset'''	) ,							'''r'''	)	)
        SCREAMING_SNAKE_CASE__											=			{int(lowerCAmelCase_	): v for k, v in idalabel.items()}
        SCREAMING_SNAKE_CASE__											=			idalabel
        SCREAMING_SNAKE_CASE__											=			{v: k for k, v in idalabel.items()}
    # size of the architecture
    if model_name in ["dino_vits8", "dino_vits16"]:
        SCREAMING_SNAKE_CASE__											=			3_8_4
        SCREAMING_SNAKE_CASE__											=			1_5_3_6
        SCREAMING_SNAKE_CASE__											=			1_2
        SCREAMING_SNAKE_CASE__											=			6

    # load original model from torch hub
    SCREAMING_SNAKE_CASE__											=			torch.hub.load('''facebookresearch/dino:main''' ,							lowerCAmelCase_	)
    original_model.eval()

    # load state_dict of original model, remove and rename some keys
    SCREAMING_SNAKE_CASE__											=			original_model.state_dict()
    if base_model:
        remove_classification_head_(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			create_rename_keys(lowerCAmelCase_ ,							base_model=lowerCAmelCase_	)
    for src, dest in rename_keys:
        rename_key(lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)
    read_in_q_k_v(lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)

    # load HuggingFace model
    if base_model:
        SCREAMING_SNAKE_CASE__											=			ViTModel(lowerCAmelCase_ ,							add_pooling_layer=lowerCAmelCase_	).eval()
    else:
        SCREAMING_SNAKE_CASE__											=			ViTForImageClassification(lowerCAmelCase_	).eval()
    model.load_state_dict(lowerCAmelCase_	)

    # Check outputs on an image, prepared by ViTImageProcessor
    SCREAMING_SNAKE_CASE__											=			ViTImageProcessor()
    SCREAMING_SNAKE_CASE__											=			image_processor(images=prepare_img() ,							return_tensors='''pt'''	)
    SCREAMING_SNAKE_CASE__											=			encoding['''pixel_values''']
    SCREAMING_SNAKE_CASE__											=			model(lowerCAmelCase_	)

    if base_model:
        SCREAMING_SNAKE_CASE__											=			original_model(lowerCAmelCase_	)
        assert torch.allclose(lowerCAmelCase_ ,							outputs.last_hidden_state[:, 0, :] ,							atol=1e-1	)
    else:
        SCREAMING_SNAKE_CASE__											=			original_model(lowerCAmelCase_	)
        assert logits.shape == outputs.logits.shape
        assert torch.allclose(lowerCAmelCase_ ,							outputs.logits ,							atol=1e-3	)

    Path(lowerCAmelCase_	).mkdir(exist_ok=lowerCAmelCase_	)
    print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''	)
    model.save_pretrained(lowerCAmelCase_	)
    print(f'''Saving image processor to {pytorch_dump_folder_path}'''	)
    image_processor.save_pretrained(lowerCAmelCase_	)


if __name__ == "__main__":
  _A					:				Optional[Any]				=			argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--model_name""",
      default="""dino_vitb16""",
      type=str,
      help="""Name of the model trained with DINO you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )
  parser.add_argument(
      """--base_model""",
      action="""store_true""",
      help="""Whether to only convert the base model (no projection head weights).""",
  )

  parser.set_defaults(base_model=True)
  _A					:				Optional[Any]				=			parser.parse_args()
  convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)







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     )
