


import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

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


if is_torch_available():
   import torch

   from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
   from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder



def     __lowerCAmelCase						(							snake_case	:    int						,      snake_case	:    int						,      snake_case	:    List[Any]						,      snake_case	:    List[Any]=None						,      snake_case	:    Union[str, Any]=None						,      snake_case	:    List[str]=None						,      snake_case	:    List[str]=None						,      snake_case	:    List[Any]=None						,      )       ->							Optional[int]:
   if attention_mask is None:
      __lowerCamelCase:				Optional[int]     =	input_ids.ne(config.pad_token_id )
   if decoder_attention_mask is None:
      __lowerCamelCase:				Tuple     =	decoder_input_ids.ne(config.pad_token_id )
   if head_mask is None:
      __lowerCamelCase:				Dict     =	torch.ones(config.encoder_layers						,      config.encoder_attention_heads						,      device=snake_case )
   if decoder_head_mask is None:
      __lowerCamelCase:				Optional[Any]     =	torch.ones(config.decoder_layers						,      config.decoder_attention_heads						,      device=snake_case )
   if cross_attn_head_mask is None:
      __lowerCamelCase:				str     =	torch.ones(config.decoder_layers						,      config.decoder_attention_heads						,      device=snake_case )
   return {
       "input_ids": input_ids,
       "decoder_input_ids": decoder_input_ids,
       "attention_mask": attention_mask,
       "decoder_attention_mask": attention_mask,
       "head_mask": head_mask,
       "decoder_head_mask": decoder_head_mask,
       "cross_attn_head_mask": cross_attn_head_mask,
   }



class        a					:





     def __init__(    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				str=13    ,	SCREAMING_SNAKE_CASE_					:				Any=7    ,	SCREAMING_SNAKE_CASE_					:				Tuple=True    ,	SCREAMING_SNAKE_CASE_					:				Tuple=False    ,	SCREAMING_SNAKE_CASE_					:				Tuple=99    ,	SCREAMING_SNAKE_CASE_					:				Any=16    ,	SCREAMING_SNAKE_CASE_					:				str=2    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=4    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=4    ,	SCREAMING_SNAKE_CASE_					:				Dict="relu"    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]=0.1    ,	SCREAMING_SNAKE_CASE_					:				str=0.1    ,	SCREAMING_SNAKE_CASE_					:				Any=0.0    ,	SCREAMING_SNAKE_CASE_					:				int=0.0    ,	SCREAMING_SNAKE_CASE_					:				Dict=20    ,	SCREAMING_SNAKE_CASE_					:				str=2    ,	SCREAMING_SNAKE_CASE_					:				int=1    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=0    ,	):
        __lowerCamelCase:				Dict     =	parent
        __lowerCamelCase:				List[Any]     =	batch_size
        __lowerCamelCase:				str     =	seq_length
        __lowerCamelCase:				List[str]     =	is_training
        __lowerCamelCase:				str     =	use_labels
        __lowerCamelCase:				List[Any]     =	vocab_size
        __lowerCamelCase:				str     =	hidden_size
        __lowerCamelCase:				Optional[Any]     =	num_hidden_layers
        __lowerCamelCase:				Optional[int]     =	num_attention_heads
        __lowerCamelCase:				Tuple     =	intermediate_size
        __lowerCamelCase:				str     =	hidden_act
        __lowerCamelCase:				Optional[int]     =	hidden_dropout_prob
        __lowerCamelCase:				Tuple     =	attention_probs_dropout_prob
        __lowerCamelCase:				Union[str, Any]     =	encoder_layerdrop
        __lowerCamelCase:				List[str]     =	decoder_layerdrop
        __lowerCamelCase:				Optional[Any]     =	max_position_embeddings
        __lowerCamelCase:				Any     =	eos_token_id
        __lowerCamelCase:				Optional[int]     =	pad_token_id
        __lowerCamelCase:				Union[str, Any]     =	bos_token_id





     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        __lowerCamelCase:				Union[str, Any]     =	ids_tensor([self.batch_size, self.seq_length]    ,	self.vocab_size							)
        __lowerCamelCase:				str     =	self.eos_token_id  # Eos Token
        __lowerCamelCase:				Optional[int]     =	ids_tensor([self.batch_size, self.seq_length]    ,	self.vocab_size							)

        # we need to clamp the input ids here to avoid having pad token in between
        # this is because for M2M100 the position_ids are prepared such that
        # all pad tokens have pos id = 2 and rest are between 2..seq_length
        # and the seq_length here is seq_length - num_pad_tokens
        # but when using past, there is no way of knowing if the past input ids had
        # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
        # position_ids being off by num_pad_tokens in past input
        __lowerCamelCase:				Optional[int]     =	input_ids.clamp(self.pad_token_id + 1							)
        __lowerCamelCase:				List[Any]     =	decoder_input_ids.clamp(self.pad_token_id + 1							)

        __lowerCamelCase:				Any     =	self.get_config()
        __lowerCamelCase:				Optional[Any]     =	prepare_mam_aaa_inputs_dict(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
        return config, inputs_dict





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        return MaMaaaConfig(
            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    ,	encoder_layerdrop=self.encoder_layerdrop    ,	decoder_layerdrop=self.decoder_layerdrop    ,	max_position_embeddings=self.max_position_embeddings    ,	eos_token_id=self.eos_token_id    ,	bos_token_id=self.bos_token_id    ,	pad_token_id=self.pad_token_id    ,	)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        __lowerCamelCase   ,     __lowerCamelCase:				Tuple     =	self.prepare_config_and_inputs()
        return config, inputs_dict





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]							):
        __lowerCamelCase:				Tuple     =	MaMaaaModel(config=SCREAMING_SNAKE_CASE_							).get_decoder().to(SCREAMING_SNAKE_CASE_							).eval()
        __lowerCamelCase:				Tuple     =	inputs_dict["""input_ids"""]
        __lowerCamelCase:				Tuple     =	inputs_dict["""attention_mask"""]
        __lowerCamelCase:				List[str]     =	inputs_dict["""head_mask"""]

        # first forward pass
        __lowerCamelCase:				int     =	model(SCREAMING_SNAKE_CASE_    ,	attention_mask=SCREAMING_SNAKE_CASE_    ,	head_mask=SCREAMING_SNAKE_CASE_    ,	use_cache=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase   ,     __lowerCamelCase:				Union[str, Any]     =	outputs.to_tuple()

        # create hypothetical multiple next token and extent to next_input_ids
        __lowerCamelCase:				List[str]     =	ids_tensor((self.batch_size, 3)    ,	config.vocab_size							)
        __lowerCamelCase:				List[Any]     =	ids_tensor((self.batch_size, 3)    ,	2							)

        # append to next input_ids and
        __lowerCamelCase:				Union[str, Any]     =	torch.cat([input_ids, next_tokens]    ,	dim=-1							)
        __lowerCamelCase:				Optional[Any]     =	torch.cat([attention_mask, next_attn_mask]    ,	dim=-1							)

        __lowerCamelCase:				List[str]     =	model(SCREAMING_SNAKE_CASE_    ,	attention_mask=SCREAMING_SNAKE_CASE_							)["""last_hidden_state"""]
        __lowerCamelCase:				str     =	model(SCREAMING_SNAKE_CASE_    ,	attention_mask=SCREAMING_SNAKE_CASE_    ,	past_key_values=SCREAMING_SNAKE_CASE_							)[
            """last_hidden_state"""
        ]

        # select random slice
        __lowerCamelCase:				Tuple     =	ids_tensor((1,)    ,	output_from_past.shape[-1]							).item()
        __lowerCamelCase:				Optional[int]     =	output_from_no_past[:, -3:, random_slice_idx].detach()
        __lowerCamelCase:				Any     =	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(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	atol=1E-2							)							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Tuple							):
        __lowerCamelCase:				Optional[int]     =	MaMaaaModel(config=SCREAMING_SNAKE_CASE_							).to(SCREAMING_SNAKE_CASE_							).eval()
        __lowerCamelCase:				str     =	model(**SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[Any]     =	outputs.encoder_last_hidden_state
        __lowerCamelCase:				str     =	outputs.last_hidden_state

        with tempfile.TemporaryDirectory() as tmpdirname:
           __lowerCamelCase:				Any     =	model.get_encoder()
           encoder.save_pretrained(SCREAMING_SNAKE_CASE_							)
           __lowerCamelCase:				List[Any]     =	MaMaaaEncoder.from_pretrained(SCREAMING_SNAKE_CASE_							).to(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[Any]     =	encoder(inputs_dict["""input_ids"""]    ,	attention_mask=inputs_dict["""attention_mask"""]							)[
            0
        ]

        self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3							)

        with tempfile.TemporaryDirectory() as tmpdirname:
           __lowerCamelCase:				List[Any]     =	model.get_decoder()
           decoder.save_pretrained(SCREAMING_SNAKE_CASE_							)
           __lowerCamelCase:				str     =	MaMaaaDecoder.from_pretrained(SCREAMING_SNAKE_CASE_							).to(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				str     =	decoder(
            input_ids=inputs_dict["""decoder_input_ids"""]    ,	attention_mask=inputs_dict["""decoder_attention_mask"""]    ,	encoder_hidden_states=SCREAMING_SNAKE_CASE_    ,	encoder_attention_mask=inputs_dict["""attention_mask"""]    ,	)[0]

        self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3							)



@require_torch
class        a					(			_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,unittest.TestCase   ):
     UpperCAmelCase__ :  List[str]						      =	(
         (
             MaMaaaModel,
             MaMaaaForConditionalGeneration,
         )
         if is_torch_available()
         else ()
     )
     UpperCAmelCase__ :  str						      =	(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
     UpperCAmelCase__ :  Any						      =	(
         {
             "conversational": MaMaaaForConditionalGeneration,
             "feature-extraction": MaMaaaModel,
             "summarization": MaMaaaForConditionalGeneration,
             "text2text-generation": MaMaaaForConditionalGeneration,
             "translation": MaMaaaForConditionalGeneration,
         }
         if is_torch_available()
         else {}
     )
     UpperCAmelCase__ :  Optional[int]						      =	True
     UpperCAmelCase__ :  List[str]						      =	True
     UpperCAmelCase__ :  Dict						      =	False
     UpperCAmelCase__ :  Optional[int]						      =	False





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				Any							):
        if pipeline_test_casse_name == "TranslationPipelineTests":
           # Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
           # `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
           return True

        return False





     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        __lowerCamelCase:				Optional[int]     =	MaMaaaModelTester(self							)
        __lowerCamelCase:				Union[str, Any]     =	ConfigTester(self    ,	config_class=SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        self.config_tester.run_common_tests()





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
        __lowerCamelCase   ,     __lowerCamelCase:				Any     =	self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
           __lowerCamelCase:				str     =	model_class(SCREAMING_SNAKE_CASE_							)

           with tempfile.TemporaryDirectory() as tmpdirname:
              model.save_pretrained(SCREAMING_SNAKE_CASE_							)
              __lowerCamelCase   ,     __lowerCamelCase:				Tuple     =	model_class.from_pretrained(SCREAMING_SNAKE_CASE_    ,	output_loading_info=SCREAMING_SNAKE_CASE_							)
           self.assertEqual(info["""missing_keys"""]    ,	[]							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        __lowerCamelCase:				List[str]     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        __lowerCamelCase:				Union[str, Any]     =	self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_encoder_decoder_model_standalone(*SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        __lowerCamelCase   ,     __lowerCamelCase:				int     =	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
           __lowerCamelCase:				str     =	model_class(SCREAMING_SNAKE_CASE_							)
           model.to(SCREAMING_SNAKE_CASE_							)
           model.eval()

           __lowerCamelCase:				str     =	copy.deepcopy(self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)							)

           if not self.is_encoder_decoder:
              __lowerCamelCase:				str     =	inputs["""input_ids"""]
              del inputs["input_ids"]
           else:
              __lowerCamelCase:				Tuple     =	inputs["""input_ids"""]
              __lowerCamelCase:				str     =	inputs.get("""decoder_input_ids"""    ,	SCREAMING_SNAKE_CASE_							)
              del inputs["input_ids"]
              inputs.pop("""decoder_input_ids"""    ,	SCREAMING_SNAKE_CASE_							)

           __lowerCamelCase:				List[str]     =	model.get_input_embeddings()
           if not self.is_encoder_decoder:
              __lowerCamelCase:				int     =	wte(SCREAMING_SNAKE_CASE_							)
           else:
              __lowerCamelCase:				Tuple     =	wte(SCREAMING_SNAKE_CASE_							)
              __lowerCamelCase:				Optional[Any]     =	wte(SCREAMING_SNAKE_CASE_							)

           with torch.no_grad():
              model(**SCREAMING_SNAKE_CASE_							)[0]







     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        __lowerCamelCase   ,     __lowerCamelCase:				Tuple     =	self.model_tester.prepare_config_and_inputs()
        __lowerCamelCase:				Optional[int]     =	input_dict["""input_ids"""]
        __lowerCamelCase:				List[Any]     =	input_ids.ne(1							).to(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Dict     =	MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE_							).eval().to(SCREAMING_SNAKE_CASE_							)
        if torch_device == "cuda":
           model.half()
        model.generate(SCREAMING_SNAKE_CASE_    ,	attention_mask=SCREAMING_SNAKE_CASE_							)
        model.generate(num_beams=4    ,	do_sample=SCREAMING_SNAKE_CASE_    ,	early_stopping=SCREAMING_SNAKE_CASE_    ,	num_return_sequences=3							)



def     __lowerCAmelCase						(							snake_case	:    Optional[Any] )       ->							Dict:
   return torch.tensor(snake_case						,      dtype=torch.long						,      device=snake_case )


_A		:				Optional[int]									=       1e-4







@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class        a					(			unittest.TestCase   ):





     @cached_property
     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        return MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M"""							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        __lowerCamelCase:				Any     =	MaMaaaModel.from_pretrained("""facebook/m2m100_418M"""							).to(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				int     =	_long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]]							)
        __lowerCamelCase:				Union[str, Any]     =	_long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]]							)
        __lowerCamelCase:				Any     =	prepare_mam_aaa_inputs_dict(model.config    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
        with torch.no_grad():
           __lowerCamelCase:				Union[str, Any]     =	model(**SCREAMING_SNAKE_CASE_							)[0]
        __lowerCamelCase:				Optional[Any]     =	torch.Size((1, 11, 1024)							)
        self.assertEqual(output.shape    ,	SCREAMING_SNAKE_CASE_							)
        # change to expected output here
        __lowerCamelCase:				Dict     =	torch.tensor(
            [[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]]    ,	device=SCREAMING_SNAKE_CASE_							)
        self.assertTrue(torch.allclose(output[:, :3, :3]    ,	SCREAMING_SNAKE_CASE_    ,	atol=SCREAMING_SNAKE_CASE_							)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        __lowerCamelCase:				int     =	MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M"""							).to(SCREAMING_SNAKE_CASE_							)

        # change to intended input
        __lowerCamelCase:				Tuple     =	_long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]]							)
        __lowerCamelCase:				Union[str, Any]     =	_long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]]							)
        __lowerCamelCase:				Union[str, Any]     =	prepare_mam_aaa_inputs_dict(model.config    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
        with torch.no_grad():
           __lowerCamelCase:				Any     =	model(**SCREAMING_SNAKE_CASE_							)[0]
        __lowerCamelCase:				Union[str, Any]     =	torch.Size((1, 11, model.config.vocab_size)							)
        self.assertEqual(output.shape    ,	SCREAMING_SNAKE_CASE_							)
        # change to expected output here
        __lowerCamelCase:				List[Any]     =	torch.tensor(
            [[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]]    ,	device=SCREAMING_SNAKE_CASE_							)
        self.assertTrue(torch.allclose(output[:, :3, :3]    ,	SCREAMING_SNAKE_CASE_    ,	atol=SCREAMING_SNAKE_CASE_							)							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase:				List[str]     =	MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M"""							).to(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Union[str, Any]     =	MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M"""    ,	src_lang="""fr"""    ,	tgt_lang="""en"""							)

        __lowerCamelCase:				Union[str, Any]     =	[
            """L'affaire NSA souligne l'absence totale de débat sur le renseignement""",
            """Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.""",
            """Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent"""
            """ Fabius convoque l'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de"""
            """ l'ampleur de la surveillance américaine sur l'ensemble des communications en France.""",
        ]

        # The below article tests that we don't add any hypotheses outside of the top n_beams
        __lowerCamelCase:				List[Any]     =	tokenizer(SCREAMING_SNAKE_CASE_    ,	padding=SCREAMING_SNAKE_CASE_    ,	return_tensors="""pt"""							)

        __lowerCamelCase:				Dict     =	model.generate(
            input_ids=dct["""input_ids"""].to(SCREAMING_SNAKE_CASE_							)    ,	attention_mask=dct["""attention_mask"""].to(SCREAMING_SNAKE_CASE_							)    ,	num_beams=5    ,	forced_bos_token_id=tokenizer.get_lang_id("""en"""							)    ,	)

        __lowerCamelCase:				Dict     =	[
            """The NSA case highlights the total absence of intelligence debate""",
            """I think there are two levels of response from the French government.""",
            """When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S."""
            """ Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all"""
            """ communications in France.""",
        ]

        __lowerCamelCase:				Any     =	tokenizer.batch_decode(
            hypotheses_batch.tolist()    ,	clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_    ,	skip_special_tokens=SCREAMING_SNAKE_CASE_							)
        assert generated == expected_en






import argparse
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import (
    RobertaTokenizer,
    TrOCRConfig,
    TrOCRForCausalLM,
    TrOCRProcessor,
    VisionEncoderDecoderModel,
    ViTConfig,
    ViTImageProcessor,
    ViTModel,
)
from transformers.utils import logging


logging.set_verbosity_info()
_A		:				List[Any]									=       logging.get_logger(__name__)



def     __lowerCAmelCase						(							snake_case	:    Any						,      snake_case	:    int )       ->							Dict:
   __lowerCamelCase:				Tuple     =	[]
   for i in range(encoder_config.num_hidden_layers ):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append(
          (f'encoder.deit.blocks.{i}.norm1.weight', f'encoder.encoder.layer.{i}.layernorm_before.weight') )
      rename_keys.append((f'encoder.deit.blocks.{i}.norm1.bias', f'encoder.encoder.layer.{i}.layernorm_before.bias') )
      rename_keys.append(
          (f'encoder.deit.blocks.{i}.attn.proj.weight', f'encoder.encoder.layer.{i}.attention.output.dense.weight') )
      rename_keys.append(
          (f'encoder.deit.blocks.{i}.attn.proj.bias', f'encoder.encoder.layer.{i}.attention.output.dense.bias') )
      rename_keys.append(
          (f'encoder.deit.blocks.{i}.norm2.weight', f'encoder.encoder.layer.{i}.layernorm_after.weight') )
      rename_keys.append((f'encoder.deit.blocks.{i}.norm2.bias', f'encoder.encoder.layer.{i}.layernorm_after.bias') )
      rename_keys.append(
          (f'encoder.deit.blocks.{i}.mlp.fc1.weight', f'encoder.encoder.layer.{i}.intermediate.dense.weight') )
      rename_keys.append(
          (f'encoder.deit.blocks.{i}.mlp.fc1.bias', f'encoder.encoder.layer.{i}.intermediate.dense.bias') )
      rename_keys.append(
          (f'encoder.deit.blocks.{i}.mlp.fc2.weight', f'encoder.encoder.layer.{i}.output.dense.weight') )
      rename_keys.append((f'encoder.deit.blocks.{i}.mlp.fc2.bias', f'encoder.encoder.layer.{i}.output.dense.bias') )

   # cls token, position embeddings and patch embeddings of encoder
   rename_keys.extend(
       [
           ("""encoder.deit.cls_token""", """encoder.embeddings.cls_token"""),
           ("""encoder.deit.pos_embed""", """encoder.embeddings.position_embeddings"""),
           ("""encoder.deit.patch_embed.proj.weight""", """encoder.embeddings.patch_embeddings.projection.weight"""),
           ("""encoder.deit.patch_embed.proj.bias""", """encoder.embeddings.patch_embeddings.projection.bias"""),
           ("""encoder.deit.norm.weight""", """encoder.layernorm.weight"""),
           ("""encoder.deit.norm.bias""", """encoder.layernorm.bias"""),
       ] )

   return rename_keys



def     __lowerCAmelCase						(							snake_case	:    str						,      snake_case	:    Union[str, Any] )       ->							Tuple:
   for i in range(encoder_config.num_hidden_layers ):
      # queries, keys and values (only weights, no biases)
      __lowerCamelCase:				Union[str, Any]     =	state_dict.pop(f'encoder.deit.blocks.{i}.attn.qkv.weight' )

      __lowerCamelCase:				Any     =	in_proj_weight[
          : encoder_config.hidden_size, :
      ]
      __lowerCamelCase:				Dict     =	in_proj_weight[
          encoder_config.hidden_size : encoder_config.hidden_size * 2, :
      ]
      __lowerCamelCase:				Any     =	in_proj_weight[
          -encoder_config.hidden_size :, :
      ]



def     __lowerCAmelCase						(							snake_case	:    Dict						,      snake_case	:    Union[str, Any]						,      snake_case	:    Optional[int] )       ->							int:
   __lowerCamelCase:				List[str]     =	dct.pop(snake_case )
   __lowerCamelCase:				Tuple     =	val



def     __lowerCAmelCase						(							snake_case	:    Dict )       ->							Any:
   if "handwritten" in checkpoint_url:
      __lowerCamelCase:				Tuple     =	"""https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg"""  # industry
      # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
      # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
      # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"  #
      # url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
   elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
      __lowerCamelCase:				Union[str, Any]     =	"""https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg"""
   __lowerCamelCase:				Union[str, Any]     =	Image.open(requests.get(snake_case						,      stream=snake_case ).raw ).convert("""RGB""" )
   return im



@torch.no_grad()
def     __lowerCAmelCase						(							snake_case	:    List[Any]						,      snake_case	:    Any )       ->							List[str]:
   __lowerCamelCase:				Union[str, Any]     =	ViTConfig(image_size=384						,      qkv_bias=snake_case )
   __lowerCamelCase:				Optional[int]     =	TrOCRConfig()

   # size of the architecture
   if "base" in checkpoint_url:
      __lowerCamelCase:				Optional[int]     =	768
   elif "large" in checkpoint_url:
      # use ViT-large encoder
      __lowerCamelCase:				Optional[int]     =	1024
      __lowerCamelCase:				int     =	4096
      __lowerCamelCase:				Tuple     =	24
      __lowerCamelCase:				Optional[Any]     =	16
      __lowerCamelCase:				str     =	1024
   else:
      raise ValueError("""Should either find 'base' or 'large' in checkpoint URL""" )

   # the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
   if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
      __lowerCamelCase:				Any     =	False
      __lowerCamelCase:				Optional[Any]     =	"""relu"""
      __lowerCamelCase:				List[Any]     =	1024
      __lowerCamelCase:				List[str]     =	True
      __lowerCamelCase:				int     =	False
      __lowerCamelCase:				int     =	False

   # load HuggingFace model
   __lowerCamelCase:				Any     =	ViTModel(snake_case						,      add_pooling_layer=snake_case )
   __lowerCamelCase:				Dict     =	TrOCRForCausalLM(snake_case )
   __lowerCamelCase:				Any     =	VisionEncoderDecoderModel(encoder=snake_case						,      decoder=snake_case )
   model.eval()

   # load state_dict of original model, rename some keys
   __lowerCamelCase:				Any     =	torch.hub.load_state_dict_from_url(snake_case						,      map_location="""cpu"""						,      check_hash=snake_case )["""model"""]

   __lowerCamelCase:				Optional[Any]     =	create_rename_keys(snake_case						,      snake_case )
   for src, dest in rename_keys:
      rename_key(snake_case						,      snake_case						,      snake_case )
   read_in_q_k_v(snake_case						,      snake_case )

   # remove parameters we don't need
   del state_dict["encoder.deit.head.weight"]
   del state_dict["encoder.deit.head.bias"]
   del state_dict["decoder.version"]

   # add prefix to decoder keys
   for key, val in state_dict.copy().items():
      __lowerCamelCase:				int     =	state_dict.pop(snake_case )
      if key.startswith("""decoder""" ) and "output_projection" not in key:
         __lowerCamelCase:				Any     =	val
      else:
         __lowerCamelCase:				List[str]     =	val

    # load state dict
   model.load_state_dict(snake_case )

   # Check outputs on an image
   __lowerCamelCase:				Any     =	ViTImageProcessor(size=encoder_config.image_size )
   __lowerCamelCase:				Tuple     =	RobertaTokenizer.from_pretrained("""roberta-large""" )
   __lowerCamelCase:				List[Any]     =	TrOCRProcessor(snake_case						,      snake_case )

   __lowerCamelCase:				Optional[int]     =	processor(images=prepare_img(snake_case )						,      return_tensors="""pt""" ).pixel_values

   # verify logits
   __lowerCamelCase:				Tuple     =	torch.tensor([[model.config.decoder.decoder_start_token_id]] )
   __lowerCamelCase:				Dict     =	model(pixel_values=snake_case						,      decoder_input_ids=snake_case )
   __lowerCamelCase:				Any     =	outputs.logits

   __lowerCamelCase:				Dict     =	torch.Size([1, 1, 50265] )
   if "trocr-base-handwritten" in checkpoint_url:
      __lowerCamelCase:				Tuple     =	torch.tensor(
          [-1.4_502, -4.6_683, -0.5_347, -2.9_291, 9.1_435, -3.0_571, 8.9_764, 1.7_560, 8.7_358, -1.5_311] )
   elif "trocr-large-handwritten" in checkpoint_url:
      __lowerCamelCase:				Optional[int]     =	torch.tensor(
          [-2.6_437, -1.3_129, -2.2_596, -5.3_455, 6.3_539, 1.7_604, 5.4_991, 1.4_702, 5.6_113, 2.0_170] )
   elif "trocr-base-printed" in checkpoint_url:
      __lowerCamelCase:				Union[str, Any]     =	torch.tensor(
          [-5.6_816, -5.8_388, 1.1_398, -6.9_034, 6.8_505, -2.4_393, 1.2_284, -1.0_232, -1.9_661, -3.9_210] )
   elif "trocr-large-printed" in checkpoint_url:
      __lowerCamelCase:				Union[str, Any]     =	torch.tensor(
          [-6.0_162, -7.0_959, 4.4_155, -5.1_063, 7.0_468, -3.1_631, 2.6_466, -0.3_081, -0.8_106, -1.7_535] )

   if "stage1" not in checkpoint_url:
      assert logits.shape == expected_shape, "Shape of logits not as expected"
      assert torch.allclose(logits[0, 0, :10]						,      snake_case						,      atol=1e-3 ), "First elements of logits not as expected"

   Path(snake_case ).mkdir(exist_ok=snake_case )
   print(f'Saving model to {pytorch_dump_folder_path}' )
   model.save_pretrained(snake_case )
   print(f'Saving processor to {pytorch_dump_folder_path}' )
   processor.save_pretrained(snake_case )


if __name__ == "__main__":
   _A		:				int									=       argparse.ArgumentParser()

   parser.add_argument(
       '''--checkpoint_url''',
       default='''https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt''',
       type=str,
       help='''URL to the original PyTorch checkpoint (.pth file).''',
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
   )
   _A		:				Optional[int]									=       parser.parse_args()
   convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


